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The ocean could profoundly modulate the ever-increasing atmospheric CO2 by air-sea CO2 exchange process, which is also able to cause significant changes of physical and biogeochemical properties in return. In this study, we assessed the long-term average and spatial-temporal variability of global air-sea CO2 exchange flux (FCO2) since 1980s basing on the results of 18 Coupled Model Intercomparison Project Phase 6 (CMIP6) Earth System Models (ESMs). Our findings indicate that the CMIP6 ESMs simulated global CO2 sink in recent three decades ranges from 1.80 to 2.24 Pg C/a, which is coincidence with the results of cotemporaneous observations. What's more, the CMIP6 ESMs consistently show that the global oceanic CO2 sink has gradually intensified since 1980s as well as the observations. This study confirms the simulated FCO2 could reach agreements with the observations in the aspect of primary climatological characteristics, however, the simulation skills of CIMP6 ESMs in diverse open-sea biomes are unevenness. None of the 18 CMIP6 ESMs could reproduce the observed FCO2 increasement in the central-eastern tropical Pacific and the midlatitude Southern Ocean. Deficiencies of some CMIP6 ESMs in reproducing the atmospheric pressure systems of the Southern Hemisphere and the El Niño-Southern Oscillation (ENSO) mode of the tropical Pacific are probably the major causes. [ABSTRACT FROM AUTHOR]

The Arctic coastal margin receives a disproportionately large fraction of the global river discharge. The bio-geochemistry of the river water as it empties into the marine environment reflects inputs and processes that occur as the water travels from its headwaters. Climate-induced changes to Arctic vegetation and permafrost melt may impact river chemistry. Understanding the impact of river nutrients on coastal marine production, and how this may change in the future, are important for resource managers and community members who monitor and rely on coastal food resources. Using the Energy Exascale Earth System Model we explore the impact of timing and river nutrient concentrations on primary production in each coastal Arctic region and then assess how this influences secondary production and particle fluxes supporting the benthic food web. Our results indicate that while the concentration of Arctic river nitrogen can have a significant impact on annual average nitrogen and primary production in the coastal Arctic, with production increases of up to 20% in the river influenced interior Seas, the timing of the river nutrient inputs into the marine environment appears less important. Bloom timing and partitioning between small and large phytoplankton were minimally impacted by both river nutrient concentration and timing, suggesting that in general, coastal Arctic ecosystem dynamics will continue to be primarily driven by light availability, rather than nutrients. Under a doubling river nutrient scenario, the percentage increase in the POC flux to the benthos on river influenced Arctic coastal shelves was 2-4 times the percentage increase in primary production, suggesting changes to the river nutrient concentration has the potential to modify the Arctic food web structure and dynamics. Generally, the nutrient-induced changes to primary production were smaller than changes previously simulated in response to ice reduction and temperature increase. However, in the Laptev Sea, the production increase resulting from a doubling of river nutrients exceeded the production increase simulated with an atmospheric warming scenario. Dissolved organic carbon is presently poorly represented in the model so its impact on production is hard to simulate. Applying established relationships between modeled DOC, total DOC, and light absorption we illustrate that DOC could play a very important role in modulating production. Our findings highlight the importance of developing more realistic river nutrient and discharge forcing for Earth System Models such that their impact on the critical Arctic coastal domain can be more adequately resolved.

The quantitative functions for climate damages provide theoretical ground for the cost-benefit analysis in climate change economics, and they are also critical for linking climate module with economic module in the Integrated Assessment Models (IAMs). Nevertheless, it is necessary for IAMs to update sectoral climate impacts in order to catch up the advance in climate change studies. This study updates the sectoral climate damage function at global scale from climate Framework for Uncertainty, Negotiation and Distribution (FUND) model and develops the aggregate climate damage function in a bottom-up fashion. Besides conventional sectors such as agriculture, forestry, water resources, energy consumption and ecosystems, this study expands climate disaster types, assesses human health impacts caused by various air pollutants, and updates coastal damage by sea level rise. The Beijing Climate Center Simple Earth System Model (BCC_SESM) is used to project climate system based on Business-as-Usual (BAU) scenario, and the 2 °C and 1.5 °C scenarios based on RCPs and SSP2 databases. Sectoral results show that the agricultural sector is projected to suffer 63% of the total damage, followed by water resources (16%) and human health (12%) sectors in 2100. The regression results indicate that the aggregate climate damage function is in positive quadratic form for zero discounting. Under BAU scenario, the aggregate climate damage is projected to be 517.7 trillion USD during 2011–2100. Compared to that, the 2 °C and 1.5 °C scenarios are projected to respectively reduce climate damages by 215.6 trillion USD (approximately 41.6%) and 263.5 trillion USD (50.9%) in 2011–2100.

Ocean biogeochemical (BGC) models utilise a large number of poorly-constrained global parameters to mimic unresolved processes and reproduce the observed complex spatio-temporal patterns. Large model errors stem primarily from inaccuracies in these parameters whose optimal values can vary both in space and time. This study aims to demonstrate the ability of ensemble data assimilation (DA) methods to provide high-quality and improved BGC parameters within an Earth system model in an idealized perfect twin experiment framework. We use the Norwegian Climate Prediction Model (NorCPM), which combines the Norwegian Earth System Model with the Dual-One-Step ahead smoothing-based Ensemble Kalman Filter (DOSA-EnKF). We aim to estimate five spatially varying BGC parameters by assimilating salinity and temperature profiles and surface BGC (Phytoplankton, Nitrate, Phosphate, Silicate, and Oxygen) observations in a strongly coupled DA framework—i.e., jointly updating ocean and BGC state-parameters during the assimilation. We show how BGC observations can effectively constrain error in the ocean physics and vice versa. The method converges quickly (less than a year) and largely reduces the errors in the BGC parameters. Some parameter error remains, but the resulting state variable error using the estimated parameters for a free ensemble run and for a reanalysis performs nearly as well as with true parameter values. Optimal parameter values can also be recovered by assimilating climatological BGC observations or sparse observational networks. The findings of this study demonstrate the applicability of the DA approach for tuning the system in a real framework.

Ocean net primary production (NPP) results from CO2 fixation by marine phytoplankton, catalysing the transfer of organic matter and energy to marine ecosystems, supporting most marine food webs, and fisheries production as well as stimulating ocean carbon sequestration. Thus, alterations to ocean NPP in response to climate change, as quantified by Earth system model experiments conducted as part of the 5th and 6th Coupled Model Intercomparison Project (CMIP5 and CMIP6) efforts, are expected to alter key ecosystem services. Despite reductions in inter-model variability since CMIP5, the ocean components of CMIP6 models disagree roughly 2-fold in the magnitude and spatial distribution of NPP in the contemporary era, due to incomplete understanding and insufficient observational constraints. Projections of NPP change in absolute terms show large uncertainty in CMIP6, most notably in the North Atlantic and the Indo-Pacific regions, with the latter explaining over two-thirds of the total inter-model uncertainty. While the Indo-Pacific has previously been identified as a hotspot for climate impacts on biodiversity and fisheries, the increased inter-model variability of NPP projections further exacerbates the uncertainties of climate risks on ocean-dependent human communities. Drivers of uncertainty in NPP changes at regional scales integrate different physical and biogeochemical factors that require more targeted mechanistic assessment in future studies. Globally, inter-model uncertainty in the projected changes in NPP has increased since CMIP5, which amplifies the challenges associated with the management of associated ecosystem services. Notably, this increased regional uncertainty in the projected NPP change in CMIP6 has occurred despite reduced uncertainty in the regional rates of NPP for historical period. Improved constraints on the magnitude of ocean NPP and the mechanistic drivers of its spatial variability would improve confidence in future changes. It is unlikely that the CMIP6 model ensemble samples the complete uncertainty in NPP, with the inclusion of additional mechanistic realism likely to widen projections further in the future, especially at regional scales. This has important consequences for assessing ecosystem impacts. Ultimately, we need an integrated mechanistic framework that considers how NPP and marine ecosystems respond to impacts of not only climate change, but also the additional non-climate drivers.

The abilities of 12 earth system models (ESMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to reproduce satellite-derived vegetation biological variables over the Tibetan Plateau (TP) were examined. The results show that most of the models tend to overestimate the observed leaf area index (LAI) and vegetation carbon above the ground, with the possible reasons being overestimation of photosynthesis and precipitation. The model simulations show a consistent increasing trend with observed LAI over most of the TP during the reference period of 1986-2005, while they fail to reproduce the downward trend around the headstream of the Yellow River shown in the observation due to their coarse resolutions. Three of the models: CCSM4, CESM1-BGC, and NorESM1-ME, which share the same vegetation model, show some common strengths and weaknesses in their simulations according to our analysis. The model ensemble indicates a reasonable spatial distribution but overestimated land coverage, with a significant decreasing trend (−1.48% per decade) for tree coverage and a slight increasing trend (0.58% per decade) for bare ground during the period 1950-2005. No significant sign of variation is found for grass. To quantify the relative performance of the models in representing the observed mean state, seasonal cycle, and interannual variability, a model ranking method was performed with respect to simulated LAI. INMCM4, bcc-csm-1.1m, MPI-ESM-LR, IPSL CM5A-LR, HadGEM2-ES, and CCSM4 were ranked as the best six models in reproducing vegetation dynamics among the 12 models. [ABSTRACT FROM AUTHOR]

Earth System Models (ESMs) are fundamental tools for understanding climate-carbon feedback. An ESM version of the Flexible Global Ocean-Atmosphere-Land System model (FGOALS) was recently developed within the IPCC AR5 Coupled Model Intercomparison Project Phase 5 (CMIP5) modeling framework, and we describe the development of this model through the coupling of a dynamic global vegetation and terrestrial carbon model with FGOALS-s2. The performance of the coupled model is evaluated as follows. The simulated global total terrestrial gross primary production (GPP) is 124.4 PgC yr and net primary production (NPP) is 50.9 PgC yr. The entire terrestrial carbon pools contain about 2009.9 PgC, comprising 628.2 PgC and 1381.6 PgC in vegetation and soil pools, respectively. Spatially, in the tropics, the seasonal cycle of NPP and net ecosystem production (NEP) exhibits a dipole mode across the equator due to migration of the monsoon rainbelt, while the seasonal cycle is not so significant in Leaf Area Index (LAI). In the subtropics, especially in the East Asian monsoon region, the seasonal cycle is obvious due to changes in temperature and precipitation from boreal winter to summer. Vegetation productivity in the northern mid-high latitudes is too low, possibly due to low soil moisture there. On the interannual timescale, the terrestrial ecosystem shows a strong response to ENSO. The model-simulated Niño3.4 index and total terrestrial NEP are both characterized by a broad spectral peak in the range of 2-7 years. Further analysis indicates their correlation coefficient reaches −0.7 when NEP lags the Niño3.4 index for about 1-2 months. [ABSTRACT FROM AUTHOR]

The article discusses the use of empirical research and process-based models in predicting the mortality of forests. It offers a comparison of the two approaches. It examines the impact of climate change and variability on forest condition. The utilization of a hybrid approach in analyzing climatic condition is also tackled.