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Todd Emmenegger
,
Yi-Hung Kuo
,
Shaocheng Xie
,
Chengzhu Zhang
,
Cheng Tao
, and
J. David Neelin

Abstract

A set of diagnostics based on simple, statistical relationships between precipitation and the thermodynamic environment in observations is implemented to assess phase 6 of the Coupled Model Intercomparison Project (CMIP6) model behavior with respect to precipitation. Observational data from the Atmospheric Radiation Measurement (ARM) permanent field observational sites are augmented with satellite observations of precipitation and temperature as an observational baseline. A robust relationship across observational datasets between column water vapor (CWV) and precipitation, in which conditionally averaged precipitation exhibits a sharp pickup at some critical CWV value, provides a useful convective onset diagnostic for climate model comparison. While a few models reproduce an appropriate precipitation pickup, most models begin their pickup at too low CWV and the increase in precipitation with increasing CWV is too weak. Convective transition statistics compiled in column relative humidity (CRH) partially compensate for model temperature biases—although imperfectly since the temperature dependence is more complex than that of column saturation. Significant errors remain in individual models and weak pickups are generally not improved. The conditional-average precipitation as a function of CRH can be decomposed into the product of the probability of raining and mean precipitation during raining times (conditional intensity). The pickup behavior is primarily dependent on the probability of raining near the transition and on the conditional intensity at higher CRH. Most models roughly capture the CRH dependence of these two factors. However, compensating biases often occur: model conditional intensity that is too low at a given CRH is compensated in part by excessive probability of precipitation.

Open access
Ning Wei
,
Jianyang Xia
,
Jian Zhou
,
Lifen Jiang
,
Erqian Cui
,
Jiaye Ping
, and
Yiqi Luo

Abstract

The spatial and temporal variations in terrestrial carbon storage play a pivotal role in regulating future climate change. However, Earth system models (ESMs), which have coupled the terrestrial biosphere and atmosphere, show great uncertainty in simulating the global land carbon storage. Here, based on multiple global datasets and a traceability analysis, we diagnosed the uncertainty source of terrestrial carbon storage in 22 ESMs that participated in phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). The modeled global terrestrial carbon storage has converged among ESMs from CMIP5 (1936.9 ± 739.3 PgC) to CMIP6 (1774.4 ± 439.0 PgC) but is persistently lower than the observation-based estimates (2285 ± 669 PgC). By further decomposing terrestrial carbon storage into net primary production (NPP) and ecosystem carbon residence time (τE ), we found that the decreased intermodel spread in land carbon storage primarily resulted from more accurate simulations on NPP among ESMs from CMIP5 to CMIP6. The persistent underestimation of land carbon storage was caused by the biased τE . In CMIP5 and CMIP6, the modeled τE was far shorter than the observation-based estimates. The potential reasons for the biased τE could be the lack of or incomplete representation of nutrient limitation, vertical soil biogeochemistry, and the permafrost carbon cycle. Moreover, the modeled τE became the key driver for the intermodel spread in global land carbon storage in CMIP6. Overall, our study indicates that CMIP6 models have greatly improved the terrestrial carbon cycle, with a decreased model spread in global terrestrial carbon storage and less uncertain productivity. However, more efforts are needed to understand and reduce the persistent data–model disagreement on carbon storage and residence time in the terrestrial biosphere.

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Yu-Fan Geng
,
Shang-Ping Xie
,
Xiao-Tong Zheng
,
Shang-Min Long
,
Sarah M. Kang
,
Xiaopei Lin
, and
Zi-Han Song

Abstract

Tropical climate response to greenhouse warming is to first order symmetric about the equator but climate models disagree on the degree of latitudinal asymmetry of the tropical change. Intermodel spread in equatorial asymmetry of tropical climate response is investigated by using 37 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6). In the simple simulation with CO2 increase at 1% per year but without aerosol forcing, this study finds that intermodel spread in tropical asymmetry is tied to that in the extratropical surface heat flux change related to the Atlantic meridional overturning circulation (AMOC) and Southern Ocean sea ice concentration (SIC). AMOC or Southern Ocean SIC change alters net energy flux at the top of the atmosphere and sea surface in one hemisphere and may induce interhemispheric atmospheric energy transport. The negative feedback of the shallow meridional overturning circulation in the tropics and the positive low cloud feedback in the subtropics are also identified. Our results suggest that reducing the intermodel spread in extratropical change can improve the reliability of tropical climate projections.

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L. Ruby Leung
,
William R. Boos
,
Jennifer L. Catto
,
Charlotte A. DeMott
,
Gill M. Martin
,
J. David Neelin
,
Travis A. O’Brien
,
Shaocheng Xie
,
Zhe Feng
,
Nicholas P. Klingaman
,
Yi-Hung Kuo
,
Robert W. Lee
,
Cristian Martinez-Villalobos
,
S. Vishnu
,
Matthew D. K. Priestley
,
Cheng Tao
, and
Yang Zhou

Abstract

Precipitation sustains life and supports human activities, making its prediction one of the most societally relevant challenges in weather and climate modeling. Limitations in modeling precipitation underscore the need for diagnostics and metrics to evaluate precipitation in simulations and predictions. While routine use of basic metrics is important for documenting model skill, more sophisticated diagnostics and metrics aimed at connecting model biases to their sources and revealing precipitation characteristics relevant to how model precipitation is used are critical for improving models and their uses. This paper illustrates examples of exploratory diagnostics and metrics including 1) spatiotemporal characteristics metrics such as diurnal variability, probability of extremes, duration of dry spells, spectral characteristics, and spatiotemporal coherence of precipitation; 2) process-oriented metrics based on the rainfall–moisture coupling and temperature–water vapor environments of precipitation; and 3) phenomena-based metrics focusing on precipitation associated with weather phenomena including low pressure systems, mesoscale convective systems, frontal systems, and atmospheric rivers. Together, these diagnostics and metrics delineate the multifaceted and multiscale nature of precipitation, its relations with the environments, and its generation mechanisms. The metrics are applied to historical simulations from phases 5 and 6 of the Coupled Model Intercomparison Project. Models exhibit diverse skill as measured by the suite of metrics, with very few models consistently ranked as top or bottom performers compared to other models in multiple metrics. Analysis of model skill across metrics and models suggests possible relationships among subsets of metrics, motivating the need for more systematic analysis to understand model biases for informing model development.

Open access
R. M. Holmes
,
T. Sohail
, and
J. D. Zika

Abstract

Anthropogenically induced radiative imbalances in the climate system lead to a slow accumulation of heat in the ocean. This warming is often obscured by natural modes of climate variability such as El Niño–Southern Oscillation (ENSO), which drive substantial ocean temperature changes as a function of depth and latitude. The use of watermass coordinates has been proposed to help isolate forced signals and filter out fast adiabatic processes associated with modes of variability. However, how much natural modes of variability project into these different coordinate systems has not been quantified. Here we apply a rigorous framework to quantify ocean temperature variability using both a quasi-Lagrangian, watermass-based temperature coordinate and Eulerian depth and latitude coordinates in a free-running climate model under preindustrial conditions. The temperature-based coordinate removes the adiabatic component of ENSO-dominated interannual variability by definition, but a substantial diabatic signal remains. At slower (decadal to centennial) frequencies, variability in the temperature- and depth-based coordinates is comparable. Spectral analysis of temperature tendencies reveals the dominance of advective processes in latitude and depth coordinates while the variability in temperature coordinates is related closely to the surface forcing. Diabatic mixing processes play an important role at slower frequencies where quasi-steady-state balances emerge between forcing and mixing in temperature, advection and mixing in depth, and forcing and advection in latitude. While watermass-based analyses highlight diabatic effects by removing adiabatic variability, our work shows that natural variability has a strong diabatic component and cannot be ignored in the analysis of long-term trends.

Significance Statement

Quantifying the ocean warming associated with anthropogenically induced radiative imbalances in the climate system can be challenging due to the superposition with modes of internal climate variability such as El Niño. One method proposed to address this issue is the analysis of temperature changes in fluid-following (or “watermass”) coordinates that filter out fast adiabatic processes associated with these modes of variability. In this study we compare a watermass-based analysis with more traditional analyses of temperature changes at fixed depth and latitude to show that even natural modes of climate variability exhibit a substantial signal in watermass coordinates, particularly at decadal and slower frequencies. This natural variability must be taken into account when analyzing long-term temperature trends in the ocean.

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