Global Earth System Research Articles

OCEANFILMS (Organic Compounds from Ecosystems to Aerosols: Natural Films and Interfaces via Langmuir Molecular Surfactants) sea spray organic aerosol emissions – implementation in a global climate model and impacts on clouds

Abstract. Sea spray aerosol is one of the major sources of atmospheric particulate matter globally. It has increasingly been recognized that organic matter derived from ocean biological precursors contributes significantly to the composition of submicron sea spray and may modify sea spray aerosol impacts on clouds and climate. This paper describes the implementation of the OCEANFILMS (Organic Compounds from Ecosystems to Aerosols: Natural Films and Interfaces via Langmuir Molecular Surfactants) parameterization for sea spray organic aerosol emissions in a global Earth system model, the Energy Exascale Earth System Model (E3SM). OCEANFILMS is a physically based model that links sea spray chemistry with ocean biogeochemistry using a Langmuir partitioning approach. We describe the implementation details of OCEANFILMS within E3SM, compare simulated aerosol fields with observations, and investigate impacts on simulated clouds and climate. Four sensitivity cases are tested, in which organic emissions either strictly add to or strictly replace sea salt emissions (in mass and number) and are either fully internally or fully externally mixed with sea salt. The simulation with internally mixed, added organics agrees reasonably well with observed seasonal cycles of organic matter in marine aerosol and has been selected as the default configuration of the E3SM. In this configuration, sea spray organic aerosol contributes an additional source of cloud condensation nuclei, adding up to 30 cm−3 to Southern Ocean boundary-layer cloud condensation nuclei concentrations (supersaturation = 0.1 %). The addition of this new aerosol source strengthens shortwave radiative cooling by clouds by −0.36 W m−2 in the global annual mean and contributes more than −3.5 W m−2 to summertime zonal mean cloud forcing in the Southern Ocean, with maximum zonal mean impacts of about −4 W m−2 around 50–60∘ S. This is consistent with a previous top-down, satellite-based empirical estimate of the radiative forcing by sea spray organic aerosol over the Southern Ocean. Through its mechanistic approach, OCEANFILMS offers a path towards improved understanding of the feedbacks between ocean biology, sea spray organic matter, and climate.

Overestimated Terrestrial Carbon Uptake in the Future Owing to the Lack of Spatial Variations CO2 in an Earth System Model

AbstractAtmospheric carbon dioxide (CO2) would be increasing much more if it were not for terrestrial carbon (C) uptake, fueling the drawdown of atmospheric CO2 in vegetation and soil on decadal to centennial time scales. Here, we used a global Earth system model (BNU‐ESM) with two different CO2 data sets (i.e., uniform CO2 vs. non‐uniform CO2 data sets) to simulate the responses of the C balance, particularly to the non‐uniform CO2 effect. Under future conditions of 2071–2100, accounting for spatial variations of CO2 concentrations resulted in 0.51 Pg C yr−1 or 19% additional global net ecosystem production (NEP) inductions relative to the uniform conditions. The reduction in NEP in the future was mostly caused by the reduction in the Northern Hemisphere, within which summer was the season that accounted for the largest fraction of this reduction. Changes in NEP under future conditions differed largely to those under present conditions, resulting from changes in the circulation caused by the non‐uniform CO2—for example, reductions in evapotranspiration limit water vapor contributions to the lower atmosphere, and substantially diminish convective precipitation, which led to decreased precipitation. Our findings call for more attention to be paid to the influence of spatial variations in CO2 concentration—particularly in the Northern Hemisphere—to better constrain the projected C uptake under future conditions. Also, it highlights the fundamental importance of non‐uniform CO2 in determining the pattern, response, and magnitude of C uptake through to 2100.

Choices in land representation materially affect modeled biofuel carbon intensity estimates

Estimates of biofuel carbon intensity are uncertain and depend on modeled land use change (LUC) emissions. While analysts have focused on economic and agronomic assumptions affecting the quantity of land converted, researchers have paid less attention to how models classify land into broad categories and designate some categories as ineligible for LUC. To explore the effect of these land representation attributes, we use three versions of a global human and Earth systems model, GCAM, and compute the “carbon intensity of land-use change” (CI-LUC) from increased U.S. corn ethanol production. We consider uncertainty in model parameters along with the choice of land representation and find the latter is one of the most influential parameters on estimated CI-LUC. A version of the model that protects 90% of non-commercial land reduced estimated CI-LUC by an average of 32% across Monte Carlo trials compared to our baseline model. Another version that mimics the GTAP-BIO-ADV land representation, which protects all non-commercial land, reduced CI-LUC by an average of 19%. The results of this experiment demonstrate that land representation in biofuel LUC models is an important determinant of CI-LUC.

Building a machine learning surrogate model for wildfire activities within a global Earth system model

Abstract. Wildfire is an important ecosystem process, influencing land biogeophysical and biogeochemical dynamics and atmospheric composition. Fire-driven loss of vegetation cover, for example, directly modifies the surface energy budget as a consequence of changing albedo, surface roughness, and partitioning of sensible and latent heat fluxes. Carbon dioxide and methane emitted by fires contribute to a positive atmospheric forcing, whereas emissions of carbonaceous aerosols may contribute to surface cooling. Process-based modeling of wildfires in Earth system land models is challenging due to limited understanding of human, climate, and ecosystem controls on fire counts, fire size, and burned area. Integration of mechanistic wildfire models within Earth system models requires careful parameter calibration, which is computationally expensive and subject to equifinality. To explore alternative approaches, we present a deep neural network (DNN) scheme that surrogates the process-based wildfire model with the Energy Exascale Earth System Model (E3SM) interface. The DNN wildfire model accurately simulates observed burned area with over 90 % higher accuracy with a large reduction in parameterization time compared with the current process-based wildfire model. The surrogate wildfire model successfully captured the observed monthly regional burned area during validation period 2011 to 2015 (coefficient of determination, R2=0.93). Since the DNN wildfire model has the same input and output requirements as the E3SM process-based wildfire model, our results demonstrate the applicability of machine learning for high accuracy and efficient large-scale land model development and predictions.

Fragmentation-Driven Divergent Trends in Burned Area in Amazonia and Cerrado

The two major Brazilian biomes, the Amazonia and the Cerrado (savanna), are increasingly exposed to fires. The Amazonian Forest is a fire sensitive ecosystem where fires are a typically rare disturbance while the Cerrado is naturally fire-dependent. Human activities, such as landscape fragmentation and land-use management, have modified the fire regime of the Cerrado and introduced fire into the Amazonian Forest. There is limited understanding of the role of landscape fragmentation on fire occurrence in the Amazonia and Cerrado biomes. Due to differences in vegetation structure, composition, and land use characteristics in each biome, we hypothesize that the emerging burned area (BA) patterns will result from biome-specific fire responses to fragmentation. The aim of this study was to test the general relationship between BA, landscape fragmentation, and agricultural land in the Amazonia and the Cerrado biomes. To estimate the trends and status of landscape fragmentation a Forest Area Density (FAD) index was calculated based on the MapBiomas land cover dataset for both biomes between 2002 and 2018. BA fraction was analyzed within native vegetation against the FAD and agricultural land fraction. Our results showed an increase in landscape fragmentation across 16% of Amazonia and 15% of Cerrado. We identified an opposite relationship between BA fraction, and landscape fragmentation and agricultural fraction contrasting the two biomes. For Amazonia, both landscape fragmentation and agricultural fraction increased BA fraction due to an increase of human ignition activities. For the Cerrado, on the other hand, an increase in landscape fragmentation and agricultural fraction caused a decrease in BA fraction within the native vegetation. For both biomes, we found that during drought years BA increases whilst the divergent trends driven by fragmentation in the two contrasting global biomes is maintained. This understanding will be critical to informing the representation of fire dynamics in fire-enable Dynamic Global Vegetation Models and Earth System Models for climate projection and future ecosystem service provision.

Oceanographic regional climate projections for the Baltic Sea until 2100

Abstract. The Baltic Sea, located in northern Europe, is a semi-enclosed, shallow and tideless sea with seasonal sea-ice cover in its northern sub-basins. Its long water residence time contributes to oxygen depletion in the bottom water of its southern sub-basins. In this study, recently performed scenario simulations for the Baltic Sea including marine biogeochemistry were analysed and compared with earlier published projections. Specifically, dynamical downscaling using a regionally coupled atmosphere–ocean climate model was used to regionalise four global Earth system models. However, as the regional climate model does not include components representing terrestrial and marine biogeochemistry, an additional catchment and a coupled physical–biogeochemical model for the Baltic Sea were included. The scenario simulations take the impact of various global sea level rise scenarios into account. According to the projections, compared to the present climate, higher water temperatures, a shallower mixed layer with a sharper thermocline during summer, less sea-ice cover and greater mixing in the northern Baltic Sea during winter can be expected. Both the frequency and the duration of marine heat waves will increase significantly, in particular in the coastal zone of the southern Baltic Sea (except in regions with frequent upwellings). Nonetheless, due to the uncertainties in the projections regarding regional winds, the water cycle and the global sea level rise, robust and statistically significant salinity changes could not be identified. The impact of a changing climate on biogeochemical cycling is predicted to be considerable but still smaller than that of plausible nutrient input changes. Implementing the proposed Baltic Sea Action Plan, a nutrient input abatement plan for the entire catchment area, would result in a significantly improved ecological status of the Baltic Sea, including reductions in the size of the hypoxic area also in a future climate, which in turn would increase the resilience of the Baltic Sea against anticipated climate change. While our findings regarding changes in heat-cycle variables mainly confirm earlier scenario simulations, they differ substantially from earlier projections of salinity and biogeochemical cycles, due to differences in experimental setups and in input scenarios for bioavailable nutrients.

OMEN-SED(-RCM) (v1.1): a pseudo-reactive continuum representation of organic matter degradation dynamics for OMEN-SED

Abstract. The Organic Matter ENabled SEDiment model (OMEN-SED) is a one-dimensional, analytical reaction–transport model for early diagenesis in marine sediments. It explicitly resolves organic matter (OM) degradation and associated biogeochemical terminal electron acceptor, reduced species and nutrient dynamics in porous media under steady-state conditions. OMEN-SED has been specifically designed for coupling to global Earth system models and the analytical solution of the coupled set of mass conservation equations ensures the computational efficiency required for such a coupling. To find an analytical solution, OMEN-SED expresses all explicitly resolved biogeochemical processes as a function of OM degradation. The original version of OMEN-SED contains a relatively simple description of OM degradation based on two reactive OM classes, a so-called 2G model. However, such a simplified approach does not fully account for the widely observed continuous decrease in organic matter reactivity with burial depth/time. The reactive continuum model that accounts for the continuous distribution of organic compounds over the reactive spectrum represents an alternative and more realistic description but cannot be easily incorporated within the general OMEN-SED framework. Here, we extend the diagenetic framework of OMEN-SED with a multi-G approximation of the reactive continuum model (RCM) of organic matter degradation by using a finite but large number of OM fractions, each characterized by a distinct reactivity. The RCM and its multi-G approximation are fully constrained by only two free parameters, a and ν, that control the initial distribution of OM compounds over the reactivity spectrum. The new model is not only able to reproduce observed pore water profiles, sediment–water interface fluxes and redox zonation across a wide range of depositional environments but also provides a more realistic description of anaerobic degradation pathways. The added functionality extends the applicability of OMEN-SED to a broader range of environments and timescales, while requiring fewer parameters to simulate a wider spectrum of OM reactivities.

Coupled modes of projected regional change in the Bering Sea from a dynamically downscaling model under CMIP6 forcing

Three different global earth system models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) were used to explore anticipated changes in the Bering Sea under high (SSP126) and low (SSP585) carbon mitigation scenarios (i.e. low and high emission scenarios), via dynamical downscaling. A multivariate pattern analysis, based on Empirical Orthogonal Functions applied to monthly time series, reveals strong coupling of changes across several biophysical variables and the global forcing itself, on both yearly and multidecadal time scales. Rising air and ocean temperatures from the global models are strongly coupled with rising regional temperatures and reduced ice cover/thickness, as well as strong changes to the phenology of the plankton food chain, including reduced biomass of large zooplankton in the fall. This method ultimately provides a compact way to estimate the changes to many regional attributes under a variety of global change scenarios. Application of this method to a broad ensemble of the CMIP6 global model air temperatures suggests that compared to present conditions, the Bering Sea shelf bottom temperatures in July will warm by an average of ∼4 degrees C by the end of the 21st century under SSP585, as compared with ∼1 degrees C under SSP126, with greatest warming focused on the outer northern shelf.

Building a machine learning surrogate model for wildfire activities within a global earth system model

Building a machine learning surrogate model for wildfire activities within a global earth system model

Using Global-Scale Earth System Models for Regional Fisheries Applications

Climate change may impact ocean ecosystems through a number of mechanisms, including shifts in primary productivity or plankton community structure, ocean acidification, and deoxygenation. These processes can be simulated with global Earth system models (ESMs), which are increasingly being used in the context of fisheries management and other living marine resource (LMR) applications. However, projections of LMR-relevant metrics such as net primary production can vary widely between ESMs, even under identical climate scenarios. Therefore, the use of ESM should be accompanied by an understanding of the structural differences in the biogeochemical sub-models within ESMs that may give rise to these differences. This review article provides a brief overview of some of the most prominent differences among the most recent generation of ESM and how they are relevant to LMR application.

The Health and Climate Benefits of Reducing Air Pollution

In a new study, researchers apply a global Earth system model to estimate the impacts of emissions reductions by sector.

TempestExtremes v2.1: a community framework for feature detection, tracking, and analysis in large datasets

Abstract. TempestExtremes (TE) is a multifaceted framework for feature detection, tracking, and scientific analysis of regional or global Earth system datasets on either rectilinear or unstructured/native grids. Version 2.1 of the TE framework now provides extensive support for examining both nodal (i.e., pointwise) and areal features, including tropical and extratropical cyclones, monsoonal lows and depressions, atmospheric rivers, atmospheric blocking, precipitation clusters, and heat waves. Available operations include nodal and areal thresholding, calculations of quantities related to nodal features such as accumulated cyclone energy and azimuthal wind profiles, filtering data based on the characteristics of nodal features, and stereographic compositing. This paper describes the core algorithms (kernels) that have been added to the TE framework since version 1.0, including algorithms for editing pointwise trajectory files, composition of fields around nodal features, generation of areal masks via thresholding and nodal features, and tracking of areal features in time. Several examples are provided of how these kernels can be combined to produce composite algorithms for evaluating and understanding common atmospheric features and their underlying processes. These examples include analyzing the fraction of precipitation from tropical cyclones, compositing meteorological fields around extratropical cyclones, calculating fractional contribution to poleward vapor transport from atmospheric rivers, and building a climatology of atmospheric blocks.

Reducing Planetary Health Risks Through Short-Lived Climate Forcer Mitigation.

Global air pollution and climate change are major threats to planetary health. These threats are strongly linked through the short‐lived climate forcers (SLCFs); ozone (O3), aerosols, and methane (CH4). Understanding the impacts of ambitious SLCF mitigation in different source emission sectors on planetary health indicators can help prioritize international air pollution control strategies. A global Earth system model is applied to quantify the impacts of idealized 50% sustained reductions in year 2005 emissions in the eight largest global anthropogenic source sectors on the SLCFs and three indicators of planetary health: global mean surface air temperature change (∆GSAT), avoided PM2.5‐related premature mortalities and gross primary productivity (GPP). The model represents fully coupled atmospheric chemistry, aerosols, land ecosystems and climate, and includes dynamic CH4. Avoided global warming is modest, with largest impacts from 50% cuts in domestic (−0.085 K), agriculture (−0.034 K), and waste/landfill (−0.033 K). The 50% cuts in energy, domestic, and agriculture sector emissions offer the largest opportunities to mitigate global PM2.5‐related health risk at around 5%–7% each. Such small global impacts underline the challenges ahead in achieving the World Health Organization aspirational goal of a 2/3 reduction in the number of deaths from air pollution by 2030. Uncertainty due to natural climate variability in PM2.5 is an important underplayed dimension in global health risk assessment that can vastly exceed uncertainty due to the concentration‐response functions at the large regional scale. Globally, cuts to agriculture and domestic sector emissions are the most attractive targets to achieve climate and health co‐benefits through SLCF mitigation.

Uncertainty Quantification and Bayesian Inference of Cloud Parameterization in the NCAR Single Column Community Atmosphere Model (SCAM6)

Uncertainty quantification (UQ) in weather and climate models is required to assess the sensitivity of their outputs to various parameterization schemes and thereby improve their consistency with observations. Herein, we present an efficient UQ and Bayesian inference for the cloud parameters of the NCAR Single Column Atmosphere Model (SCAM6) using surrogate models based on a polynomial chaos expansion. The use of a surrogate model enables to efficiently propagate uncertainties in parameters into uncertainties in model outputs. We investigated eight uncertain parameters: the auto-conversion size threshold for ice to snow (dcs), the fall speed parameter for stratiform cloud ice (ai), the fall speed parameter for stratiform snow (as), the fall speed parameter for cloud water (ac), the collection efficiency of aggregation ice (eii), the efficiency factor of the Bergeron effect (berg_eff), the threshold maximum relative humidity for ice clouds (rhmaxi), and the threshold minimum relative humidity for ice clouds (rhmini). We built two surrogate models using two non-intrusive methods: spectral projection (SP) and basis pursuit denoising (BPDN). Our results suggest that BPDN performs better than SP as it enables to filter out internal noise during the process of fitting the surrogate model. Five out of the eight parameters (namely dcs, ai, rhmaxi, rhmini, and eii) account for most of the variance in predicted climate variables (e.g., total precipitation, cloud distribution, shortwave and longwave cloud radiative effect, ice, and liquid water path). A first-order sensitivity analysis reveals that dcs contributes ~40–80% of the total variance of the climate variables, ai around 15–30%, and rhmaxi, rhmini, and eii around 5–15%. The second- and higher-order effects contribute ~7 and 20%, respectively. The sensitivity of the model to these parameters was further explored using response curves. A Markov chain Monte Carlo (MCMC) sampling algorithm was also implemented for the Bayesian inference of dcs, ai, as, rhmini, and berg_eff using cloud distribution data collected at the Southern Great Plains (USA). The inferred parameters suggest improvements in the global Climate Earth System Model (CESM2) simulations of the tropics and sub-tropics.

Variations and influencing factors of potential evapotranspiration in large Siberian river basins during 1975–2014

Potential evapotranspiration (PET) is a key factor involved in regional and global Earth system processes. This is particularly important for Arctic region, which faces amplified warming and is vulnerable to climate change. In this study, we estimate the PET during 1975–2014 at 190 meteorological stations in Siberian river basins using three frequently used models including a combination model (Penman–Monteith, PM), a radiation-based model (Priestley–Taylor, PT), and a temperature-based model (Hamon, H). Based on the estimated PET, the spatiotemporal variations, and differences among the three models are analyzed, and the PET changes are attributed to the changes in meteorological drivers. The results show that the annual values estimated by PM and PT models are close and are much larger than that estimated by H model. The trends of PET from PM model are generally insignificant while these from H model exhibit widespread increase in much of the Yenisey and Lena river basins. The contributions of changes in air temperature and net radiation to PET changes are less than 20% at most of stations, while the increasing VPD and decreasing wind speed contribute more than 80% to the changes of PET. This finding highlights the importance of VPD and wind speed in PET estimates in the Siberian river basins. Using different PET models can substantially affect estimates of aridity index. It further indicates that cautions must be taken when selecting PET model for hydrological and climate change studies in the arctic region.

Global Changes in Secondary Atmospheric Pollutants During the 2020 COVID-19 Pandemic.

We use the global Community Earth System Model to investigate the response of secondary pollutants (ozone O3, secondary organic aerosols SOA) in different parts of the world in response to modified emissions of primary pollutants during the COVID‐19 pandemic. We quantify the respective effects of the reductions in NOx and in volatile organic carbon (VOC) emissions, which, in most cases, affect oxidants in opposite ways. Using model simulations, we show that the level of NOx has been reduced by typically 40% in China during February 2020 and by similar amounts in many areas of Europe and North America in mid‐March to mid‐April 2020, in good agreement with space and surface observations. We show that, relative to a situation in which the emission reductions are ignored and despite the calculated increase in hydroxyl and peroxy radicals, the ozone concentration increased only in a few NOx‐saturated regions (northern China, northern Europe, and the US) during the winter months of the pandemic when the titration of this molecule by NOx was reduced. In other regions, where ozone is NOx‐controlled, the concentration of ozone decreased. SOA concentrations decrease in response to the concurrent reduction in the NOx and VOC emissions. The model also shows that atmospheric meteorological anomalies produced substantial variations in the concentrations of chemical species during the pandemic. In Europe, for example, a large fraction of the ozone increase in February 2020 was associated with meteorological anomalies, while in the North China Plain, enhanced ozone concentrations resulted primarily from reduced emissions of primary pollutants.

Anticipation-induced social tipping: can the environment be stabilised by social dynamics?

In the past decades, human activities caused global Earth system changes, e.g., climate change or biodiversity loss. Simultaneously, these associated impacts have increased environmental awareness within societies across the globe, thereby leading to dynamical feedbacks between the social and natural Earth system. Contemporary modelling attempts of Earth system dynamics rarely incorporate such co-evolutions and interactions are mostly studied unidirectionally through direct or remembered past impacts. Acknowledging that societies have the additional capability for foresight, this work proposes a conceptual feedback model of socio-ecological co-evolution with the specific construct of anticipation acting as a mediator between the social and natural system. Our model reproduces results from previous sociological threshold models with bistability if one assumes a static environment. Once the environment changes in response to societal behaviour, the system instead converges towards a globally stable, but not necessarily desired, attractor. Ultimately, we show that anticipation of future ecological states then leads to metastability of the system where desired states can persist for a long time. We thereby demonstrate that foresight and anticipation form an important mechanism which, once its time horizon becomes large enough, fosters social tipping towards behaviour that can stabilise the environment and prevents potential socio-ecological collapse.

A Dynamically Downscaled Ensemble of Future Projections for the California Current System

Given the ecological and economic importance of eastern boundary upwelling systems like the California Current System (CCS), their evolution under climate change is of considerable interest for resource management. However, the spatial resolution of global earth system models (ESMs) is typically too coarse to properly resolve coastal winds and upwelling dynamics that are key to structuring these ecosystems. Here we use a high-resolution (0.1°) regional ocean circulation model coupled with a biogeochemical model to dynamically downscale ESMs and produce climate projections for the CCS under the high emission scenario, Representative Concentration Pathway 8.5. To capture model uncertainty in the projections, we downscale three ESMs: GFDL-ESM2M, HadGEM2-ES, and IPSL-CM5A-MR, which span the CMIP5 range for future changes in both the mean and variance of physical and biogeochemical CCS properties. The forcing of the regional ocean model is constructed with a “time-varying delta” method, which removes the mean bias of the ESM forcing and resolves the full transient ocean response from 1980 to 2100. We found that all models agree in the direction of the future change in offshore waters: an intensification of upwelling favorable winds in the northern CCS, an overall surface warming, and an enrichment of nitrate and corresponding decrease in dissolved oxygen below the surface mixed layer. However, differences in projections of these properties arise in the coastal region, producing different responses of the future biogeochemical variables. Two of the models display an increase of surface chlorophyll in the northern CCS, consistent with a combination of higher nitrate content in source waters and an intensification of upwelling favorable winds. All three models display a decrease of chlorophyll in the southern CCS, which appears to be driven by decreased upwelling favorable winds and enhanced stratification, and, for the HadGEM2-ES forced run, decreased nitrate content in upwelling source waters in nearshore regions. While trends in the downscaled models reflect those in the ESMs that force them, the ESM and downscaled solutions differ more for biogeochemical than for physical variables.

A machine-learning approach to human footprint index estimation with applications to sustainable development

The human footprint index (HFI) is an extensively used tool for interpreting the accelerating pressure of humanity on Earth. Up to now, the process of creating the HFI has required significant data and modeling, and updated versions of the index often lag the present day by many years. Here we introduce a near-present, global-scale machine learning-based HFI (ml-HFI) which is capable of routine update using satellite imagery alone. We present the most up-to-date map of the HFI, and document changes in human pressure during the past 20 years (2000–2019). Moreover, we demonstrate its utility as a monitoring tool for the United Nations Sustainable Development Goal 15 (SDG15), ‘Life on Land’, which aims to foster sustainable development while conserving biodiversity. We identify 43 countries that are making progress toward SDG15 while also experiencing increases in their ml-HFI. We examine a subset of these in the context of conservation policies that may or may not enable continued progress toward SDG15. This has immediate policy relevance, since the majority of countries globally are not on track to achieve Goal 15 by the declared deadline of 2030. Moving forward, the ml-HFI may be used for ongoing monitoring and evaluation support toward the twin goals of fostering a thriving society and global Earth system.

The Navy's Earth System Prediction Capability: A New Global Coupled Atmosphere‐Ocean‐Sea Ice Prediction System Designed for Daily to Subseasonal Forecasting

AbstractThis paper describes the new global Navy Earth System Prediction Capability (Navy‐ESPC) coupled atmosphere‐ocean‐sea ice prediction system developed at the Naval Research Laboratory (NRL) for operational forecasting for timescales of days to the subseasonal. Two configurations of the system are validated: (1) a low‐resolution 16‐member ensemble system and (2) a high‐resolution deterministic system. The Navy‐ESPC ensemble system became operational in August 2020, and this is the first time the NRL operational partner, Fleet Numerical Meteorology and Oceanography Center, will provide global coupled atmosphere‐ocean‐sea ice forecasts, with atmospheric forecasts extending past 16 days, and ocean and sea ice ensemble forecasts. A unique aspect of the Navy‐ESPC is that the global ocean model is eddy resolving at 1/12° in the ensemble and at 1/25° in the deterministic configurations. The component models are current Navy operational systems: NAVy Global Environmental Model (NAVGEM) for the atmosphere, HYbrid Coordinate Ocean Model (HYCOM) for the ocean, and Community Ice CodE (CICE) for the sea ice. Physics updates to improve the simulation of equatorial phenomena, particularly the Madden‐Julian Oscillation (MJO), were introduced into NAVGEM. The low‐resolution ensemble configuration and high‐resolution deterministic configuration are evaluated based on analyses and forecasts from January 2017 to January 2018. Navy‐ESPC ensemble forecast skill for large‐scale atmospheric phenomena, such as the MJO, North Atlantic Oscillation (NAO), Antarctic Oscillation (AAO), and other indices, is comparable to that of other numerical weather prediction (NWP) centers. Ensemble forecasts of ocean sea surface temperatures perform better than climatology in the tropics and midlatitudes out to 60 days. In addition, the Navy‐ESPC Pan‐Arctic and Pan‐Antarctic sea ice extent predictions perform better than climatology out to about 45 days, although the skill is dependent on season.