The magnitude and evolution of parameters that characterize feedbacks in the coupled carbon–climate system are compared across nine Earth system models (ESMs). The analysis is based on results from biogeochemically, radiatively, and fully coupled simulations in which CO2 increases at a rate of 1% yr−1. These simulations are part of phase 5 of the Coupled Model Intercomparison Project (CMIP5). The CO2 fluxes between the atmosphere and underlying land and ocean respond to changes in atmospheric CO2 concentration and to changes in temperature and other climate variables. The carbon–concentration and carbon–climate feedback parameters characterize the response of the CO2 flux between the atmosphere and the underlying surface to these changes. Feedback parameters are calculated using two different approaches. The two approaches are equivalent and either may be used to calculate the contribution of the feedback terms to diagnosed cumulative emissions. The contribution of carbon–concentration feedback to...

Carbon-concentration and carbon-climate feedbacks in CMIP5 Earth system models

. The main advancements of the Beijing Climate Center (BCC) climate system model from
phase 5 of the Coupled Model Intercomparison Project (CMIP5) to phase
6 (CMIP6) are presented, in terms of physical parameterizations and model
performance. BCC-CSM1.1 and BCC-CSM1.1m are the two models involved in CMIP5,
whereas BCC-CSM2-MR, BCC-CSM2-HR, and BCC-ESM1.0 are the three models configured for
CMIP6. Historical simulations from 1851 to 2014 from BCC-CSM2-MR (CMIP6) and
from 1851 to 2005 from BCC-CSM1.1m (CMIP5) are used for models assessment.
The evaluation matrices include the following: (a) the energy budget at
top-of-atmosphere; (b) surface air temperature, precipitation, and atmospheric circulation for
the global and East Asia regions; (c) the sea surface temperature (SST) in the
tropical Pacific; (d) sea-ice extent and thickness and Atlantic Meridional
Overturning Circulation (AMOC); and (e) climate variations at different timescales, such as the global warming trend in the 20th century, the stratospheric
quasi-biennial oscillation (QBO), the Madden–Julian Oscillation (MJO), and the diurnal
cycle of precipitation. Compared with BCC-CSM1.1m, BCC-CSM2-MR shows
significant improvements in many aspects including the tropospheric air
temperature and circulation at global and regional scales in East Asia and
climate variability at different timescales, such as the QBO, the MJO, the diurnal cycle
of precipitation, interannual variations of SST in the equatorial Pacific,
and the long-term trend of surface air temperature.

/pdf/the-beijing-climate-center-climate-system-model-bcc-csm-the-1agm2ik24a.pdf

The Beijing Climate Center Climate System Model (BCC-CSM): the main progress from CMIP5 to CMIP6

The collection of Earth system models available in the archive of phase 5 of CMIP (CMIP5) represents, at least to some degree, a sample of uncertainty of future climate evolution. The presence of duplicated code as well as shared forcing and validation data in the multiple models in the archive raises at least three potential problems: biases in the mean and variance, the overestimation of sample size, and the potential for spurious correlations to emerge in the archive because of model replication. Analytical evidence is presented to demonstrate that the distribution of models in the CMIP5 archive is not consistent with a random sample, and a weighting scheme is proposed to reduce some aspects of model codependency in the ensemble. A method is proposed for selecting diverse and skillful subsets of models in the archive, which could be used for impact studies in cases where physically consistent joint projections of multiple variables (and their temporal and spatial characteristics) are required.

/pdf/a-representative-democracy-to-reduce-interdependency-in-a-16r3tzalqr.pdf

A Representative Democracy to Reduce Interdependency in a Multimodel Ensemble

. Soil is an important regulator of Earth system processes,
but remains one of the least well-described data layers in Earth system
models (ESMs). We reviewed global soil property maps from the perspective of
ESMs, including soil physical and chemical and biological properties, which
can also offer insights to soil data developers and users. These soil
datasets provide model inputs, initial variables, and benchmark datasets. For
modelling use, the dataset should be geographically continuous and scalable and
have uncertainty estimates. The popular soil datasets used in ESMs are often
based on limited soil profiles and coarse-resolution soil-type maps with
various uncertainty sources. Updated and comprehensive soil information
needs to be incorporated into ESMs. New generation soil datasets derived
through digital soil mapping with abundant, harmonized, and quality-controlled soil observations and environmental covariates are preferred to
those derived through the linkage method (i.e. taxotransfer rule-based
method) for ESMs. SoilGrids has the highest accuracy and resolution among
the global soil datasets, while other recently developed datasets offer
useful compensation. Because there is no universal pedotransfer function, an
ensemble of them may be more suitable for providing derived soil properties to
ESMs. Aggregation and upscaling of soil data are needed for model use, but
can be avoided by using a subgrid method in ESMs at the expense of increases
in model complexity. Producing soil property maps in a time series still remains
challenging. The uncertainties in soil data need to be estimated and
incorporated into ESMs.

/pdf/a-review-of-the-global-soil-property-maps-for-earth-system-uonlv889uu.pdf

A review of the global soil property maps for Earth system models

This paper assesses intermodel spread in the slope of global-mean precipitation change ΔP with respect to surface temperature change. The ambiguous estimates in the literature for this slope are reconciled by analyzing four experiments from phase 5 of CMIP (CMIP5) and considering different definitions of the slope. The smallest intermodel spread (a factor of 1.5 between the highest and lowest estimate) is found when using a definition that disentangles temperature-independent precipitation changes (the adjustments) from the slope of the temperature-dependent precipitation response; here this slope is referred to as the hydrological sensitivity parameter η. The estimates herein show that η is more robust than stated in most previous work. The authors demonstrate that adjustments and η estimated from a steplike quadrupling CO2 experiment serve well to predict ΔP in a transient CO2 experiment. The magnitude of η is smaller in the coupled ocean–atmosphere quadrupling CO2 experiment than in the noncoup...

Understanding the Intermodel Spread in Global-Mean Hydrological Sensitivity*

This paper reviews recent progress in the development of the Beijing Climate Center Climate System Model (BCC_CSM) and its four component models (atmosphere, land surface, ocean, and sea ice). Two recent versions are described: BCC_CSM1.1 with coarse resolution (approximately 2.8125°×2.8125°) and BCC_CSM1.1(m) with moderate resolution (approximately 1.125°×1.125°). Both versions are fully coupled climate-carbon cycle models that simulate the global terrestrial and oceanic carbon cycles and include dynamic vegetation. Both models well simulate the concentration and temporal evolution of atmospheric CO2 during the 20th century with anthropogenic CO2 emissions prescribed. Simulations using these two versions of the BCC_CSM model have been contributed to the Coupled Model Intercomparison Project phase five (CMIP5) in support of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5). These simulations are available for use by both national and international communities for investigating global climate change and for future climate projections. Simulations of the 20th century climate using BCC_CSM1.1 and BCC_CSM1.1(m) are presented and validated, with particular focus on the spatial pattern and seasonal evolution of precipitation and surface air temperature on global and continental scales. Simulations of climate during the last millennium and projections of climate change during the next century are also presented and discussed. Both BCC_CSM1.1 and BCC_CSM1.1(m) perform well when compared with other CMIP5 models. Preliminary analyses indicate that the higher resolution in BCC_CSM1.1(m) improves the simulation of mean climate relative to BCC_CSM1.1, particularly on regional scales.

An overview of BCC climate system model development and application for climate change studies

Big data has emerged as the next technological revolution in IT industry after cloud computing and the Internet of Things. With the development of climate observing systems, particularly satellite meteorological observation and high-resolution climate models, and the rapid growth in the volume of climate data, climate prediction is now entering the era of big data. The application of big data will provide new ideas and methods for the continuous development of climate prediction. The rapid integration, cloud storage, cloud computing, and full-sample analysis of massive climate data makes it possible to understand climate states and their evolution more objectively, thus predicting the future climate more accurately. This paper describes the application status of big data in operational climate prediction in China; it analyzes the key big data technologies, discusses the future development of climate prediction operations from the perspective of big data, speculates on the prospects for applying climatic big data in cloud computing and data assimilation, and puts forward the notion of big data-based super-ensemble climate prediction methods and computerbased deep learning climate prediction methods.

Lianchun Song

Papers

An overview of BCC climate system model development and application for climate change studies

Operational climate prediction in the era of big data in China: Reviews and prospects