Arctic sea ice concentration and thickness data assimilation in the FIO-ESM climate forecast system

To improve the Arctic sea ice forecast skill of the First Institute of Oceanography-Earth System Model(FIO-ESM)climate forecast system,satellite-derived sea ice concentration and sea ice thickness from the Pan-Arctic IceOcean Modeling and Assimilation System(PIOMAS)are assimilated into this system,using the method of localized error subspace transform ensemble Kalman filter(LESTKF).Five-year(2014–2018)Arctic sea ice assimilation experiments and a 2-month near-real-time forecast in August 2018 were conducted to study the roles of ice data assimilation.Assimilation experiment results show that ice concentration assimilation can help to get better modeled ice concentration and ice extent.All the biases of ice concentration,ice cover,ice volume,and ice thickness can be reduced dramatically through ice concentration and thickness assimilation.The near-real-time forecast results indicate that ice data assimilation can improve the forecast skill significantly in the FIO-ESM climate forecast system.The forecasted Arctic integrated ice edge error is reduced by around 1/3 by sea ice data assimilation.Compared with the six near-real-time Arctic sea ice forecast results from the subseasonal-toseasonal(S2 S)Prediction Project,FIO-ESM climate forecast system with LESTKF ice data assimilation has relatively high Arctic sea ice forecast skill in 2018 summer sea ice forecast.Since sea ice thickness in the PIOMAS is updated in time,it is a good choice for data assimilation to improve sea ice prediction skills in the near-realtime Arctic sea ice seasonal prediction.

Eastern equatorial Pacific SST seasonal cycle in global climate models: from CMIP5 to CMIP6

The sea surface temperature(SST)seasonal cycle in the eastern equatorial Pacific(EEP)plays an important role in the El Nino–Southern Oscillation(ENSO)phenomenon.However,the reasonable simulation of SST seasonal cycle in the EEP is still a challenge for climate models.In this paper,we evaluated the performance of 17 CMIP6 climate models in simulating the seasonal cycle in the EEP and compared them with 43 CMIP5 climate models.In general,only CESM2 and SAM0-UNICON are able to successfully capture the annual mean SST characteristics,and the results showed that CMIP6 models have no fundamental improvement in the model annual mean bias.For the seasonal cycle,14 out of 17 climate models are able to represent the major characteristics of the observed SST annual evolution.In spring,12 models capture the 1–2 months leading the eastern equatorial Pacific region 1(EP1;5°S–5°N,110°–85°W)against the eastern equatorial Pacific region 2(EP2;5°S–5°N,140°–110°W).In autumn,only two models,GISS-E2-G and SAM0-UNICON,correctly show that the EP1 and EP2 SSTs vary in phase.For the CMIP6 MME SST simulation in EP1,both the cold bias along the equator in the warm phase and the warm bias in the cold phase lead to a weaker annual SST cycle in the CGCMs,which is similar to the CMIP5 results.However,both the seasonal cold bias and warm bias are considerably decreased for CMIP6,which leads the annual SST cycle to more closely reflect the observation.For the CMIP6 MME SST simulation in EP2,the amplitude is similar to the observed value due to the quasi-constant cold bias throughout the year,although the cold bias is clearly improved after August compared with CMIP5 models.Overall,although SAM0-UNICON successfully captured the seasonal cycle characteristics in the EEP and the improvement from CMIP5 to CMIP6 in simulating EEP SST is clear,the fundamental climate models simulated biases still exist.

FIO-ESM v2.0 CORE2-forced experiment for the CMIP6 Ocean Model Intercomparison Project

We introduced the Coupled Model Intercomparison Project Phase 6(CMIP6)Ocean Model Intercomparison Project CORE2-forced(OMIP-1)experiment by using the First Institute of Oceanography Earth System Model version 2.0(FIO-ESM v2.0),and comprehensively evaluated the simulation results.Unlike other OMIP models,FIO-ESM v2.0 includes a coupled ocean surface wave component model that takes into account non-breaking surface wave-induced vertical mixing in the ocean and effect of surface wave Stokes drift on air-sea momentum and heat fluxes in the climate system.A sub-layer sea surface temperature(SST)diurnal cycle parameterization was also employed to take into account effect of SST diurnal cycle on air-sea heat fluxes to improve simulations of air-sea interactions.Evaluations show that mean values and long-term trends of significant wave height were adequately reproduced in the FIO-ESM v2.0 OMIP-1 simulations,and there is a reasonable fit between the SST diurnal cycle obtained from in situ observations and that parameterized by FIO-ESM v2.0.Evaluations of model drift,temperature,salinity,mixed layer depth,and the Atlantic Meridional Overturning Circulation show that the model performs well in the FIO-ESM v2.0 OMIP-1 simulation.However,the summer sea ice extent of the Arctic and Antarctic is underestimated.

The role of bias correction on subseasonal prediction of Arctic sea ice during summer 2018

Subseasonal Arctic sea ice prediction is highly needed for practical services including icebreakers and commercial ships,while limited by the capability of climate models.A bias correction methodology in this study was proposed and performed on raw products from two climate models,the First Institute Oceanography Earth System Model(FIOESM)and the National Centers for Environmental Prediction(NCEP)Climate Forecast System(CFS),to improve 60 days predictions for Arctic sea ice.Both models were initialized on July 1,August 1,and September 1 in 2018.A 60-day forecast was conducted as a part of the official sea ice service,especially for the ninth Chinese National Arctic Research Expedition(CHINARE)and the China Ocean Shipping(Group)Company(COSCO)Northeast Passage voyages during the summer of 2018.The results indicated that raw products from FIOESM underestimated sea ice concentration(SIC)overall,with a mean bias of SIC up to 30%.Bias correction resulted in a 27%improvement in the Root Mean Square Error(RMSE)of SIC and a 10%improvement in the Integrated Ice Edge Error(IIEE)of sea ice edge(SIE).For the CFS,the SIE overestimation in the marginal ice zone was the dominant features of raw products.Bias correction provided a 7%reduction in the RMSE of SIC and a 17%reduction in the IIEE of SIE.In terms of sea ice extent,FIOESM projected a reasonable minimum time and amount in mid-September;however,CFS failed to project both.Additional comparison with subseasonal to seasonal(S2S)models suggested that the bias correction methodology used in this study was more effective when predictions had larger biases.

Simulations and Projections of Winter Sea Ice in the Barents Sea by CMIP6 Climate Models

Dramatic changes in the sea ice characteristics in the Barents Sea have potential consequences for the weather and climate systems of mid-latitude continents,Arctic ecosystems,and fisheries,as well as Arctic maritime navigation.Simulations and projections of winter sea ice in the Barents Sea based on the latest 41 climate models from the Coupled Model Intercomparison Project Phase 6(CMIP6)are investigated in this study.Results show that most CMIP6 models overestimate winter sea ice in the Barents Sea and underestimate its decreasing trend.The discrepancy is mainly attributed to the simulation bias towards an overly weak ocean heat transport through the Barents Sea Opening and the underestimation of its increasing trend.The methods of observation-based model selection and emergent constraint were used to project future winter sea ice changes in the Barents Sea.Projections indicate that sea ice in the Barents Sea will continue to decline in a warming climate and that a winter ice-free Barents Sea will occur for the first time during 2042-2089 under the Shared Socioeconomic Pathway 585(SSP5-8.5).Even in the observation-based selected models,the sensitivity of winter sea ice in the Barents Sea to global warming is weaker than observed,indicating that a winter ice-free Barents Sea might occur earlier than projected by the CMIP6 simulations.

FIO-ESM v2.0 Outputs for the CMIP6 Global Monsoons Model Intercomparison Project Experiments

Three tiers of experiments in the Global Monsoons Model Intercomparison Project(GMMIP),one of the endorsed model intercomparison projects of phase 6 of the Coupled Model Intercomparison Project(CMIP6),are implemented by the First Institute of Oceanography Earth System Model version 2(FIO-ESM v2.0),following the GMMIP protocols.Evaluation of global mean surface air temperature from 1870 to 2014 and climatological precipitation(1979–2014)in tier-1 shows that the atmosphere model of FIO-ESM v2.0 can reproduce the basic observed atmospheric features.In tier-2,the internal variability is captured by the coupled model,with the SST restoring to the model climatology plus the observed anomalies in the tropical Pacific and North Atlantic.Simulation of the Northern Hemisphere summer monsoon circulation is significantly improved by the SST restoration in the North Atlantic.In tier-3,five orographic perturbation experiments are conducted covering the period 1979–2014 by modifying the surface elevation or vertical heating in the prescribed region.In particular,the strength of the South Asian summer monsoon is reduced by removing the topography or thermal forcing above 500 m over the Asian continent.Monthly and daily simulated outputs of FIO-ESM v2.0 are provided through the Earth System Grid Federation(ESGF)node to contribute to a better understanding of the global monsoon system.

Link between the Barents Oscillation and Recent Boreal Winter Cooling over the Asian Midlatitudes

The link between boreal winter cooling over the midlatitudes of Asia and the Barents Oscillation(BO) since the late 1980s is discussed in this study, based on five datasets. Results indicate that there is a large-scale boreal winter cooling during 1990–2015 over the Asian midlatitudes, and that it is a part of the decadal oscillations of long-term surface air temperature(SAT)anomalies. The SAT anomalies over the Asian midlatitudes are significantly correlated with the BO in boreal winter. When the BO is in its positive phase, anomalously high sea level pressure over the Barents region, with a clockwise wind anomaly,causes cold air from the high latitudes to move over the midlatitudes of Asia, resulting in anomalous cold conditions in that region. Therefore, the recent increasing trend of the BO has contributed to recent winter cooling over the Asian midlatitudes.

Development of Climate and Earth System Models in China: Past Achievements and New CMIP6 Results

The Earth–Climate System Model(ECSM)is an important platform for multi-disciplinary and multi-sphere integration research,and its development is at the frontier of international geosciences,especially in the field of global change.The research and development(R&D)of ECSM in China began in the 1980 s and have achieved great progress.In China,ECSMs are now mainly developed at the Chinese Academy of Sciences,ministries,and universities.Following a brief review of the development history of Chinese ECSMs,this paper summarized the technical characteristics of nine Chinese ECSMs participating in the Coupled Model Intercomparison Project Phase 6 and preliminarily assessed the basic performances of four Chinese models in simulating the global climate and the climate in East Asia.The projected changes of global precipitation and surface air temperature and the associated relationship with the equilibrium climate sensitivity under four shared socioeconomic path scenarios were also discussed.Finally,combined with the international situation,from the perspective of further improvement,eight directions were proposed for the future development of Chinese ECSMs.

Could CMIP6 climate models reproduce the early-2000s global warming slowdown?

The unexpected global warming slowdown during 1998–2013 challenges the existing scientific understanding of global temperature change mechanisms,and thus the simulation and prediction ability of state-of-the-art climate models since most models participating in phase 5 of the Coupled Model Intercomparison Project(CMIP5)cannot simulate it.Here,we examine whether the new-generation climate models in CMIP6 can reproduce the recent global warming slowdown,and further evaluate their capacities for simulating key-scale natural variabilities which are the most likely causes of the slowdown.The results show that although the CMIP6 models present some encouraging improvements when compared with CMIP5,most of them still fail to reproduce the warming slowdown.They considerably overestimate the warming rate observed in 1998–2013,exhibiting an obvious warming acceleration rather than the observed deceleration.This is probably associated with their deficiencies in simulating the distinct temperature change signals from the human-induced long-term warming trend and/or the three crucial natural variabilities at interannual,interdecadal,and multidecadal scales.In contrast,the 4 models that can successfully reproduce the slowdown show relatively high skills in simulating the long-term warming trend and the three keyscale natural variabilities.Our work may provide important insight for the simulation and prediction of near-term climate changes.