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2020 Vol. 37, No. 1

Reducing Uncertainties in Climate Projections with Emergent Constraints: Concepts, Examples and Prospects
Florent BRIENT
2020, 37(1): 1-15. doi: 10.1007/s00376-019-9140-8
Models disagree on a significant number of responses to climate change, such as climate feedback, regional changes, or the strength of equilibrium climate sensitivity. Emergent constraints aim to reduce these uncertainties by finding links between the inter-model spread in an observable predictor and climate projections. In this paper, the concepts underlying this framework are recalled with an emphasis on the statistical inference used for narrowing uncertainties, and a review of emergent constraints found in the last two decades. Potential links between highlighted predictors are explored, especially those targeting uncertainty reductions in climate sensitivity, cloud feedback, and changes of the hydrological cycle. Yet the disagreement across emergent constraints suggests that the spread in climate sensitivity can not be significantly narrowed. This calls for weighting the realism of emergent constraints by quantifying the level of physical understanding explaining the relationship. This would also permit more efficient model evaluation and better targeted model development. In the context of the upcoming CMIP6 model intercomparison a growing number of new predictors and uncertainty reductions is expected, which call for robust statistical inferences that allow cross-validation of more likely estimates.
News & Views
Constraining the Emergent Constraints
Jianhua LU
2020, 37(1): 16-17. doi: 10.1007/s00376-019-9205-8
Data Description Article
CAS FGOALS-f3-L Model Datasets for CMIP6 GMMIP Tier-1 and Tier-3 Experiments
Bian HE, Yimin LIU, Guoxiong WU, Qing BAO, Tianjun ZHOU, Xiaofei WU, Lei WANG, Jiandong LI, Xiaocong WANG, Jinxiao LI, Wenting HU, Xiaoqi ZHANG, Chen SHENG, Yiqiong TANG
2020, 37(1): 18-28. doi: 10.1007/s00376-019-9085-y
The Chinese Academy of Sciences (CAS) Flexible Global Ocean–Atmosphere–Land System (FGOALS-f3-L) model datasets prepared for the sixth phase of the Coupled Model Intercomparison Project (CMIP6) Global Monsoons Model Intercomparison Project (GMMIP) Tier-1 and Tier-3 experiments are introduced in this paper, and the model descriptions, experimental design and model outputs are demonstrated. There are three simulations in Tier-1, with different initial states, and five simulations in Tier-3, with different topographies or surface thermal status. Specifically, Tier-3 contains four orographic perturbation experiments that remove the Tibetan–Iranian Plateau, East African and Arabian Peninsula highlands, Sierra Madre, and Andes, and one thermal perturbation experiment that removes the surface sensible heating over the Tibetan–Iranian Plateau and surrounding regions at altitudes above 500 m. These datasets will contribute to CMIP6’s value as a benchmark to evaluate the importance of long-term and short-term trends of the sea surface temperature in monsoon circulations and precipitation, and to a better understanding of the orographic impact on the global monsoon system over highlands.
Original Paper
Predicting June Mean Rainfall in the Middle/Lower Yangtze River Basin
Gill M. MARTIN, Nick J. DUNSTONE, Adam A. SCAIFE, Philip E. BETT
2020, 37(1): 29-41. doi: 10.1007/s00376-019-9051-8
We demonstrate that there is significant skill in the GloSea5 operational seasonal forecasting system for predicting June mean rainfall in the middle/lower Yangtze River basin up to four months in advance. Much of the rainfall in this region during June is contributed by the mei-yu rain band. We find that similar skill exists for predicting the East Asian summer monsoon index (EASMI) on monthly time scales, and that the latter could be used as a proxy to predict the regional rainfall. However, there appears to be little to be gained from using the predicted EASMI as a proxy for regional rainfall on monthly time scales compared with predicting the rainfall directly. Although interannual variability of the June mean rainfall is affected by synoptic and intraseasonal variations, which may be inherently unpredictable on the seasonal forecasting time scale, the major influence of equatorial Pacific sea surface temperatures from the preceding winter on the June mean rainfall is captured by the model through their influence on the western North Pacific subtropical high. The ability to predict the June mean rainfall in the middle and lower Yangtze River basin at a lead time of up to 4 months suggests the potential for providing early information to contingency planners on the availability of water during the summer season.
Modeling Arctic Boundary Layer Cloud Streets at Grey-zone Resolutions
Hui-Wen LAI, Fuqing ZHANG, Eugene E. CLOTHIAUX, David R. STAUFFER, Brian J. GAUDET, Johannes VERLINDE, Deliang CHEN
2020, 37(1): 42-56. doi: 10.1007/s00376-019-9105-y
To better understand how model resolution affects the formation of Arctic boundary layer clouds, we investigated the influence of grid spacing on simulating cloud streets that occurred near Utqiaġvik (formerly Barrow), Alaska, on 2 May 2013 and were observed by MODIS (the Moderate Resolution Imaging Spectroradiometer). The Weather Research and Forecasting model was used to simulate the clouds using nested domains with increasingly fine resolution ranging from a horizontal grid spacing of 27 km in the boundary-layer-parameterized mesoscale domain to a grid spacing of 0.111 km in the large-eddy-permitting domain. We investigated the model-simulated mesoscale environment, horizontal and vertical cloud structures, boundary layer stability, and cloud properties, all of which were subsequently used to interpret the observed roll-cloud case. Increasing model resolution led to a transition from a more buoyant boundary layer to a more shear-driven turbulent boundary layer. The clouds were stratiform-like in the mesoscale domain, but as the model resolution increased, roll-like structures, aligned along the wind field, appeared with ever smaller wavelengths. A stronger vertical water vapor gradient occurred above the cloud layers with decreasing grid spacing. With fixed model grid spacing at 0.333 km, changing the model configuration from a boundary layer parameterization to a large-eddy-permitting scheme produced a more shear-driven and less unstable environment, a stronger vertical water vapor gradient below the cloud layers, and the wavelengths of the rolls decreased slightly. In this study, only the large-eddy-permitting simulation with gird spacing of 0.111 km was sufficient to model the observed roll clouds.
A Hybrid Statistical-Dynamical Downscaling of Air Temperature over Scandinavia Using the WRF Model
Jianfeng WANG, Ricardo M. FONSECA, Kendall RUTLEDGE, Javier MARTÍN-TORRES, Jun YU
2020, 37(1): 57-74. doi: 10.1007/s00376-019-9091-0
An accurate simulation of air temperature at local scales is crucial for the vast majority of weather and climate applications. In this work, a hybrid statistical–dynamical downscaling method and a high-resolution dynamical-only downscaling method are applied to daily mean, minimum and maximum air temperatures to investigate the quality of local-scale estimates produced by downscaling. These two downscaling approaches are evaluated using station observation data obtained from the Finnish Meteorological Institute over a near-coastal region of western Finland. The dynamical downscaling is performed with the Weather Research and Forecasting (WRF) model, and the statistical downscaling method implemented is the Cumulative Distribution Function-transform (CDF-t). The CDF-t is trained using 20 years of WRF-downscaled Climate Forecast System Reanalysis data over the region at a 3-km spatial resolution for the central month of each season. The performance of the two methods is assessed qualitatively, by inspection of quantile-quantile plots, and quantitatively, through the Cramer-von Mises, mean absolute error, and root-mean-square error diagnostics. The hybrid approach is found to provide significantly more skillful forecasts of the observed daily mean and maximum air temperatures than those of the dynamical-only downscaling (for all seasons). The hybrid method proves to be less computationally expensive, and also to give more skillful temperature forecasts (at least for the Finnish near-coastal region).
Wave-Breaking Features of Blocking over Central Siberia and Its Impacts on the Precipitation Trend over Southeastern Lake Baikal
Dorina CHYI, Zuowei XIE, Ning SHI, Pinwen GUO, Huijun WANG
2020, 37(1): 75-89. doi: 10.1007/s00376-019-9048-3
Precipitation over southeastern Lake Baikal features a significant decreasing trend in July and August over 1979–2018 and is closely related to blocking occurrence over central Siberia (45°–70°N, 75°–115°E). This study investigates the formation and maintenance of anticyclonic and cyclonic wave-breaking (AWB and CWB) blocking events and their climate impacts on precipitation in the southeastern Lake Baikal area. Both AWB and CWB blocking events are characterized by a cold trough deepening from the sub-Arctic region and a ridge amplifying toward its north over central Siberia, as well as an evident Rossby wave train over midlatitude Eurasia. For AWB blocking events, the ridge and trough pair tilts clockwise and the wave train exhibits a zonal distribution. In contrast, ridge and trough pair associated with CWB blocking events leans anticlockwise with larger-scale, meridional, and more anisotropic signatures. Moreover, the incoming Rossby wave energy associated with CWB blocking events is more evident than for AWB blocking events. Therefore, CWB blocking events are more persistent. AWB blocking events produce more extensive and persistent precipitation over the southeastern Lake Baikal area than CWB blocking events, in which moderate above-normal rainfall is seen in the decaying periods of blockings. A significant decreasing trend is found in terms of AWB blocking occurrence over central Siberia, which may contribute to the downward trend of precipitation over southeastern Lake Baikal.
Non-Gaussian Lagrangian Stochastic Model for Wind Field Simulation in the Surface Layer
Chao LIU, Li FU, Dan YANG, David R. MILLER, Junming WANG
2020, 37(1): 90-104. doi: 10.1007/s00376-019-9052-7
Wind field simulation in the surface layer is often used to manage natural resources in terms of air quality, gene flow (through pollen drift), and plant disease transmission (spore dispersion). Although Lagrangian stochastic (LS) models describe stochastic wind behaviors, such models assume that wind velocities follow Gaussian distributions. However, measured surface-layer wind velocities show a strong skewness and kurtosis. This paper presents an improved model, a non-Gaussian LS model, which incorporates controllable non-Gaussian random variables to simulate the targeted non-Gaussian velocity distribution with more accurate skewness and kurtosis. Wind velocity statistics generated by the non-Gaussian model are evaluated by using the field data from the Cooperative Atmospheric Surface Exchange Study, October 1999 experimental dataset and comparing the data with statistics from the original Gaussian model. Results show that the non-Gaussian model improves the wind trajectory simulation by stably producing precise skewness and kurtosis in simulated wind velocities without sacrificing other features of the traditional Gaussian LS model, such as the accuracy in the mean and variance of simulated velocities. This improvement also leads to better accuracy in friction velocity (i.e., a coupling of three-dimensional velocities). The model can also accommodate various non-Gaussian wind fields and a wide range of skewness–kurtosis combinations. Moreover, improved skewness and kurtosis in the simulated velocity will result in a significantly different dispersion for wind/particle simulations. Thus, the non-Gaussian model is worth applying to wind field simulation in the surface layer.
Improvement of X-Band Polarization Radar Melting Layer Recognition by the Bayesian Method and ITS Impact on Hydrometeor Classification
Jianli MA, Zhiqun HU, Meilin YANG, Siteng LI
2020, 37(1): 105-116. doi: 10.1007/s00376-019-9007-z
Using melting layer (ML) and non-melting layer (NML) data observed with the X-band dual linear polarization Doppler weather radar (X-POL) in Shunyi, Beijing, the reflectivity (ZH), differential reflectivity (ZDR), and correlation coefficient (CC) in the ML and NML are obtained in several stable precipitation processes. The prior probability density distributions (PDDs) of the ZH, ZDR and CC are calculated first, and then the probabilities of ZH, ZDR and CC at each radar gate are determined (PBB in the ML and PNB in the NML) by the Bayesian method. When PBB > PNB the gate belongs to the ML, and when PBB < PNB the gate belongs to the NML. The ML identification results with the Bayesian method are contrasted under the conditions of the independent PDDs and joint PDDs of the ZH, ZDR and CC. The results suggest that MLs can be identified effectively, although there are slight differences between the two methods. Because the values of the polarization parameters are similar in light rain and dry snow, it is difficult for the polarization radar to distinguish them. After using the Bayesian method to identify the ML, light rain and dry snow can be effectively separated with the X-POL observed data.
Simulations of Microphysics and Precipitation in a Stratiform Cloud Case over Northern China: Comparison of Two Microphysics Schemes
Tuanjie HOU, Hengchi LEI, Zhaoxia HU, Jiefan YANG, Xingyu LI
2020, 37(1): 117-129. doi: 10.1007/s00376-019-8257-0
Using the Weather Research and Forecasting (WRF) model with two different microphysics schemes, the Predicted Particle Properties (P3) and the Morrison double-moment parameterizations, we simulated a stratiform rainfall event on 20–21 April 2010. The simulation output was compared with precipitation and aircraft observations. The aircraft-observed moderate-rimed dendrites and plates indicated that riming contributed significantly to ice particle growth at the mature precipitation stage. Observations of dendrite aggregation and capped columns suggested that aggregation coexisted with deposition or riming and played an important role in producing many large particles. The domain-averaged values of the 24-h surface precipitation accumulation from the two schemes were quite close to each other. However, differences existed in the temporal and spatial evolutions of the precipitation distribution. An analysis of the surface precipitation temporal evolution indicated faster precipitation in Morrison, while P3 indicated slower rainfall by shifting the precipitation pattern eastward toward what was observed. The differences in precipitation values between the two schemes were related to the cloud water content distribution and fall speeds of rimed particles. P3 simulated the stratiform precipitation event better as it captured the gradual transition in the mass-weighted fall speeds and densities from unrimed to rimed particles.