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Both the attribution of historical change and future projections of droughts rely heavily on climate modeling. However, reasonable drought simulations have remained a challenge, and the related performances of the current state-of-the-art Coupled Model Intercomparison Project phase 6 (CMIP6) models remain unknown. Here, both the strengths and weaknesses of CMIP6 models in simulating droughts and corresponding hydrothermal conditions in drylands are assessed. While the general patterns of simulated meteorological elements in drylands resemble the observations, the annual precipitation is overestimated by ∼33% (with a model spread of 2.3%–77.2%), along with an underestimation of potential evapotranspiration (PET) by ∼32% (17.5%–17.2%). The water deficit condition, measured by the difference between precipitation and PET, is 50% (29.1%–71.7%) weaker than observations. The CMIP6 models show weaknesses in capturing the climate mean drought characteristics in drylands, particularly with the occurrence and duration largely underestimated in the hyperarid Afro-Asian areas. Nonetheless, the drought-associated meteorological anomalies, including reduced precipitation, warmer temperatures, higher evaporative demand, and increased water deficit conditions, are reasonably reproduced. The simulated magnitude of precipitation (water deficit) associated with dryland droughts is overestimated by 28% (24%) compared to observations. The observed increasing trends in drought fractional area, occurrence, and corresponding meteorological anomalies during 1980–2014 are reasonably reproduced. Still, the increase in drought characteristics, associated precipitation and water deficit are obviously underestimated after the late 1990s, especially for mild and moderate droughts, indicative of a weaker response of dryland drought changes to global warming in CMIP6 models. Our results suggest that it is imperative to employ bias correction approaches in drought-related studies over drylands by using CMIP6 outputs. [ABSTRACT FROM AUTHOR]

Thermal processes on the Tibetan Plateau (TP) influence atmospheric conditions on regional and global scales. Given this, previous work has shown that soil moisture–driven surface flux variations feed back onto the atmosphere. Whilst soil moisture is a source of atmospheric predictability, no study has evaluated soil moisture–atmosphere coupling on the TP in general circulation models (GCMs). In this study, we use several analysis techniques to assess soil moisture-atmosphere coupling in CMIP6 simulations including: instantaneous coupling indices; analysis of flux and atmospheric behaviour during dry spells; and a quantification of the preference for convection over drier soils. Through these metrics we partition feedbacks into their atmospheric and terrestrial components. Consistent with previous global studies, we conclude substantial inter-model differences in the representation of soil moisture–atmosphere coupling, and that most models underestimate such feedbacks. Focusing on dry spell analysis, most models underestimate increased sensible heat during periods of rainfall deficiency. For example, the model-mean bias in anomalous sensible heat flux is 10 W m−2 (≈25%) smaller compared to observations. Deficient dry-spell sensible heat fluxes lead to a weaker atmospheric response. We also find that most GCMs fail to capture the negative feedback between soil moisture and deep convection. The poor simulation of feedbacks in CMIP6 experiments suggests that forecast models also struggle to exploit soil moisture–driven predictability. To improve the representation of land–atmosphere feedbacks requires developments in not only atmospheric modelling, but also surface processes, as we find weak relationships between rainfall biases and coupling indexes. [ABSTRACT FROM AUTHOR]

Landfalling typhoons can cause disasters over large regions. The government and emergency responders need to take measures to mitigate disasters according to the forecast of landfall position, while slight timing error can be ignored. The reliability of operational model forecasts of typhoon landfall position needs to be evaluated beforehand, according to the forecasts and observation of historical cases. In the evaluation of landfalling typhoon track, the traditional method based on point-to-point matching methods could be influenced by the predicted typhoon translation speed. Consequently, the traditional track evaluation method may result in a large track error even if the predicted landfall position is close to observation. The purpose of this paper is to address the above issue using a simple evaluation method of landfalling typhoon track forecast based on the time neighborhood approach. In this new method, the timing error was lessened to highlight the importance of the position error during the landfall of typhoon. The properties of the time neighborhood method are compared with the traditional method based on numerical forecast results of 12 landfalling typhoon cases. Results demonstrated that the new method is not sensitive to the sampling frequency, and that the difference between the time neighborhood and traditional method will be more obvious when the moving speed of typhoon is moderate (between 15–30 km h−1). The time neighborhood concept can be easily extended to a broader context when one attempts to examine the position error more than the timing error. [ABSTRACT FROM AUTHOR]

The simulated Arctic sea ice drift and its relationship with the near-surface wind and surface ocean current during 1979–2014 in nine models from China that participated in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) are examined by comparison with observational and reanalysis datasets. Most of the models reasonably represent the Beaufort Gyre (BG) and Transpolar Drift Stream (TDS) in the spatial patterns of their long-term mean sea ice drift, while the detailed location, extent, and strength of the BG and TDS vary among the models. About two-thirds of the models agree with the observation/reanalysis in the sense that the sea ice drift pattern is consistent with the near-surface wind pattern. About the same proportion of models shows that the sea ice drift pattern is consistent with the surface ocean current pattern. In the observation/reanalysis, however, the sea ice drift pattern does not match well with the surface ocean current pattern. All nine models missed the observational widespread sea ice drift speed acceleration across the Arctic. For the Arctic basin-wide spatial average, five of the nine models overestimate the Arctic long-term (1979–2014) mean sea ice drift speed in all months. Only FGOALS-g3 captures a significant sea ice drift speed increase from 1979 to 2014 both in spring and autumn. The increases are weaker than those in the observation. This evaluation helps assess the performance of the Arctic sea ice drift simulations in these CMIP6 models from China. [ABSTRACT FROM AUTHOR]

Medium to long-term precipitation forecasting plays a pivotal role in water resource management and development of warning systems. Recently, the Copernicus Climate Change Service (C3S) database has been releasing monthly forecasts for lead times of up to three months for public use. This study evaluated the ensemble forecasts of three C3S models over the period 1993–2017 in Iran's eight classified precipitation clusters for one- to three-month lead times. Probabilistic and non-probabilistic criteria were used for evaluation. Furthermore, the skill of selected models was analyzed in dry and wet periods in different precipitation clusters. The results indicated that the models performed best in western precipitation clusters, while in the northern humid cluster the models had negative skill scores. All models were better at forecasting upper-tercile events in dry seasons and lower-tercile events in wet seasons. Moreover, with increasing lead time, the forecast skill of the models worsened. In terms of forecasting in dry and wet years, the forecasts of the models were generally close to observations, albeit they underestimated several severe dry periods and overestimated a few wet periods. Moreover, the multi-model forecasts generated via multivariate regression of the forecasts of the three models yielded better results compared with those of individual models. In general, the ECMWF and UKMO models were found to be appropriate for one-month-ahead precipitation forecasting in most clusters of Iran. For the clusters considered in Iran and for the long-range system versions considered, the Météo France model had lower skill than the other models. [ABSTRACT FROM AUTHOR]

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 localscale 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). [ABSTRACT FROM AUTHOR]

The CarbonTracker (CT) model has been used in previous studies for understanding and predicting the sources, sinks, and dynamics that govern the distribution of atmospheric CO2 at varying ranges of spatial and temporal scales. However, there are still challenges for reproducing accurate model-simulated CO2 concentrations close to the surface, typically associated with high spatial heterogeneity and land cover. In the present study, we evaluated the performance of nested-grid CT model simulations of CO2 based on the CT2016 version through comparison with in-situ observations over East Asia covering the period 2009–13. We selected sites located in coastal, remote, inland, and mountain areas. The results are presented at diurnal and seasonal time periods. At target stations, model agreement with in-situ observations was varied in capturing the diurnal cycle. Overall, biases were less than 6.3 ppm on an all-hourly mean basis, and this was further reduced to a maximum of 4.6 ppm when considering only the daytime. For instance, at Anmyeondo, a small bias was obtained in winter, on the order of 0.2 ppm. The model revealed a diurnal amplitude of CO2 that was nearly flat in winter at Gosan and Anmyeondo stations, while slightly overestimated in the summertime. The model's performance in reproducing the diurnal cycle remains a challenge and requires improvement. The model showed better agreement with the observations in capturing the seasonal variations of CO2 during daytime at most sites, with a correlation coefficient ranging from 0.70 to 0.99. Also, model biases were within −0.3 and 1.3 ppm, except for inland stations (7.7 ppm). [ABSTRACT FROM AUTHOR]

In this study, the CERES (Crop Estimation through Resource and Environment Synthesis) crop model was coupled with CLM3.5, the land module of the regional climate model RegCM4. The new coupled model was named RegCM4 CERES; and in this model, crop type was further divided into winter wheat, spring wheat, spring maize, summer maize, early rice, late rice, single rice, and other crop types based on each distribution fraction. The development of each crop sub-type was simulated by the corresponding crop model separately, with each planting and harvesting date. A simulation test using RegCM4 CERES was conducted across China from 1999 to 2008; a control test was also performed using the original RegCM4. Data on crop LAI (leaf area index), soil moisture at 10 cm depth, precipitation, and 2 m air temperature were collected to evaluate the performance of RegCM4 CERES. The evaluation provided comparison of single-station time series, regional distributions, seasonal variations, and statistical indices for RegCM4 CERES. The results revealed that the coupled model had an excellent ability to simulate the phonological changes and spatial variations in crops. The consideration of dynamic crop development in RegCM4 CERES corrected the wet bias of the original RegCM4 over North China and the cold bias over South China. However, the degree of improvement was minimal and the statistical indices for RegCM4 CERES were roughly the same as the original RegCM4. [ABSTRACT FROM AUTHOR]

Evaluating a climate model’s fidelity (ability to simulate observed climate) is a critical step in establishing confidence in the model’s suitability for future climate projections, and in tuning climate model parameters. Model developers use their judgement in determining which trade-offs between different aspects of model fidelity are acceptable. However, little is known about the degree of consensus in these evaluations, and whether experts use the same criteria when different scientific objectives are defined. Here, we report on results from a broad community survey studying expert assessments of the relative importance of different output variables when evaluating a global atmospheric model’s mean climate. We find that experts adjust their ratings of variable importance in response to the scientific objective, for instance, scientists rate surface wind stress as significantly more important for Southern Ocean climate than for the water cycle in the Asian watershed. There is greater consensus on the importance of certain variables (e.g., shortwave cloud forcing) than others (e.g., aerosol optical depth). We find few differences in expert consensus between respondents with greater or less climate modeling experience, and no statistically significant differences between the responses of climate model developers and users. The concise variable lists and community ratings reported here provide baseline descriptive data on current expert understanding of certain aspects of model evaluation, and can serve as a starting point for further investigation, as well as developing more sophisticated evaluation and scoring criteria with respect to specific scientific objectives. [ABSTRACT FROM AUTHOR]

Since the interaction between atmospheric synoptic eddy (SE) (2-8 days) activity and low-frequency (LF) (monthly) flow (referred to as SELF) plays an essential role in generating and maintaining dominant climate modes, an evaluation of the performance of BCC_CSM1.1(m) in simulating the SE feedback onto the LF flow is given in this paper. The model captures well the major spatial features of climatological eddy vorticity forcing, eddy-induced growth rate, and patterns of SELF feedback for the climate modes with large magnitudes in cold seasons and small magnitudes in warm seasons for both the Northern and Southern Hemisphere. As in observations, the eddy-induced growth rate and SELF feedback patterns in the model also show positive SE feedback. Overall, the relationships between SE and LF flow show that BCC_CSM1.1(m) satisfactorily captures the basic features of positive SE feedback, which demonstrates the simulation skill of the model for LF variability. Specifically, such an evaluation can help to find model biases of BCC_CSM1.1(m) in simulating SE feedback, which will provide a reference for the model's application. [ABSTRACT FROM AUTHOR]

Given the crucial role of land surface processes in global and regional climates, there is a pressing need to test and verify the performance of land surface models via comparisons to observations. In this study, the eddy covariance measurements from 20 FLUXNET sites spanning more than 100 site-years were utilized to evaluate the performance of the Common Land Model (CoLM) over different vegetation types in various climate zones. A decomposition method was employed to separate both the observed and simulated energy fluxes, i.e., the sensible heat flux, latent heat flux, net radiation, and ground heat flux, at three timescales ranging from stepwise (30 min) to monthly. A comparison between the simulations and observations indicated that CoLM produced satisfactory simulations of all four energy fluxes, although the different indexes did not exhibit consistent results among the different fluxes. A strong agreement between the simulations and observations was found for the seasonal cycles at the 20 sites, whereas CoLM underestimated the latent heat flux at the sites with distinct dry and wet seasons, which might be associated with its weakness in simulating soil water during the dry season. CoLM cannot explicitly simulate the midday depression of leaf gas exchange, which may explain why CoLM also has a maximum diurnal bias at noon in the summer. Of the eight selected vegetation types analyzed, CoLM performs best for evergreen broadleaf forests and worst for croplands and wetlands. [ABSTRACT FROM AUTHOR]

The planetary boundary layer turbulence and moist convection parameterizations have been modified recently in the NASA Goddard Institute for Space Studies (GISS) Model E2 atmospheric general circulation model (GCM; post-CMIP5, hereafter P5). In this study, single column model (SCM P5) simulated cloud fractions (CFs), cloud liquid water paths (LWPs) and precipitation were compared with Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) groundbased observations made during the period 2002-08. CMIP5 SCM simulations and GCM outputs over the ARM SGP region were also used in the comparison to identify whether the causes of cloud and precipitation biases resulted from either the physical parameterization or the dynamic scheme. The comparison showed that the CMIP5 SCM has difficulties in simulating the vertical structure and seasonal variation of low-level clouds. The new scheme implemented in the turbulence parameterization led to significantly improved cloud simulations in P5. It was found that the SCM is sensitive to the relaxation time scale. When the relaxation time increased from 3 to 24 h, SCM P5-simulated CFs and LWPs showed a moderate increase (10%-20%) but precipitation increased significantly (56%), which agreed better with observations despite the less accurate atmospheric state. Annual averages among the GCM and SCM simulations were almost the same, but their respective seasonal variations were out of phase. This suggests that the same physical cloud parameterization can generate similar statistical results over a long time period, but different dynamics drive the differences in seasonal variations. This study can potentially provide guidance for the further development of the GISS model. [ABSTRACT FROM AUTHOR]

Based on the simulations of 31 global models in CMIP5, the performance of the models in simulating the Hadley and Walker circulations is evaluated. In addition, their change in intensity by the end of the 21st century (2080-2099) under the RCP4.5 and RCP8.5 scenarios, relative to 1986-2005, is analyzed from the perspective of 200 hPa velocity potential. Validation shows good performance of the individual CMIP5 models and the multi-model ensemble mean (MME) in reproducing the meridional (zonal) structure and magnitude of Hadley (Walker) circulation. The MME can also capture the observed strengthening tendency of the winter Hadley circulation and weakening tendency of the Walker circulation. Such secular trends can be simulated by 39% and 74% of the models, respectively. The MME projection indicates that the winter Hadley circulation and the Walker circulation will weaken under both scenarios by the end of the 21st century. The weakening amplitude is larger under RCP8.5 than RCP4.5, due to stronger external forcing. The majority of the CMIP5 models show the same projection as the MME. However, for the summer Hadley circulation, the MME shows little change under RCP4.5 and large intermodel spread is apparent. Around half of the models project an increase, and the other half project a decrease. Under the RCP8.5 scenario, the MME and 65% of the models project a weakening of the summer southern Hadley circulation. [ABSTRACT FROM AUTHOR]

The climatological mean state, seasonal variation and long-term upward trend of 1979-2005 latent heat flux (LHF) in historical runs of 14 coupled general circulation models from CMIP5 (Coupled Model Intercomparison Project Phase 5) are evaluated against OAFlux (Objectively Analyzed air-sea Fluxes) data. Inter-model diversity of these models in simulating the annual mean climatological LHF is discussed. Results show that the models can capture the climatological LHF fairly well, but the amplitudes are generally overestimated. Model-simulated seasonal variations of LHF match well with observations with overestimated amplitudes. The possible origins of these biases are wind speed biases in the CMIP5 models. Inter-model diversity analysis shows that the overall stronger or weaker LHF over the tropical and subtropical Pacific region, and the meridional variability of LHF, are the two most notable diversities of the CMIP5 models. Regression analysis indicates that the inter-model diversity may come from the diversity of simulated SST and near-surface atmospheric specific humidity. Comparing the observed long-term upward trend, the trends of LHF and wind speed are largely underestimated, while trends of SST and air specific humidity are grossly overestimated, which may be the origins of the model biases in reproducing the trend of LHF. [ABSTRACT FROM AUTHOR]

Given that climate extremes in China might have serious regional and global consequences, an increasing number of studies are examining temperature extremes in China using the Coupled Model Intercomparison Project Phase 5 (CMIP5) models. This paper investigates recent changes in temperature extremes in China using 25 state-of-the-art global climate models participating in CMIP5. Thirteen indices that represent extreme temperature events were chosen and derived by daily maximum and minimum temperatures, including those representing the intensity (absolute indices and threshold indices), duration (duration indices), and frequency (percentile indices) of extreme temperature. The overall performance of each model is summarized by a 'portrait' diagram based on relative root-mean-square error, which is the RMSE relative to the median RMSE of all models, revealing the multi-model ensemble simulation to be better than individual model for most indices. Compared with observations, the models are able to capture the main features of the spatial distribution of extreme temperature during 1986-2005. Overall, the CMIP5 models are able to depict the observed indices well, and the spatial structure of the ensemble result is better for threshold indices than frequency indices. The spread amongst the CMIP5 models in different subregions for intensity indices is small and the median CMIP5 is close to observations; however, for the duration and frequency indices there can be wide disagreement regarding the change between models and observations in some regions. The model ensemble also performs well in reproducing the observational trend of temperature extremes. All absolute indices increase over China during 1961-2005. [ABSTRACT FROM AUTHOR]

Sea level rise (SLR) is one of the major socioeconomic risks associated with global warming. Mass losses from the Greenland ice sheet (GrIS) will be partially responsible for future SLR, although there are large uncertainties in modeled climate and ice sheet behavior. We used the ice sheet model SICOPOLIS (SImulation COde for POLythermal Ice Sheets) driven by climate projections from 20 models in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) to estimate the GrIS contribution to global SLR. Based on the outputs of the 20 models, it is estimated that the GrIS will contribute 0-16 (0-27) cm to global SLR by 2100 under the Representative Concentration Pathways (RCP) 4.5 (RCP 8.5) scenarios. The projected SLR increases further to 7-22 (7-33) cm with 2×basal sliding included. In response to the results of the multimodel ensemble mean, the ice sheet model projects a global SLR of 3 cm and 7 cm (10 cm and 13 cm with 2×basal sliding) under the RCP 4.5 and RCP 8.5 scenarios, respectively. In addition, our results suggest that the uncertainty in future sea level projection caused by the large spread in climate projections could be reduced with model-evaluation and the selective use of model outputs. [ABSTRACT FROM AUTHOR]

A fast version of the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG)/Institute of Atmospheric Physics (IAP) climate system model is briefly documented. The fast coupled model employs a low resolution version of the atmospheric component Grid Atmospheric Model of IAP/LASG (GAMIL), with the other parts of the model, namely an oceanic component LASG/IAP Climate Ocean Model (LICOM), land component Common Land Model (CLM), and sea ice component from National Center for Atmospheric Research Community Climate System Model (NCAR CCSM2), as the same as in the standard version of LASG/IAP Flexible Global Ocean Atmosphere Land System model (FGOALS g). The parameterizations of physical and dynamical processes of the atmospheric component in the fast version are identical to the standard version, although some parameter values are different. However, by virtue of reduced horizontal resolution and increased time-step of the most time-consuming atmospheric component, it runs faster by a factor of 3 and can serve as a useful tool for longterm and large-ensemble integrations. A 1000-year control simulation of the present-day climate has been completed without flux adjustments. The final 600 years of this simulation has virtually no trends in global mean sea surface temperatures and is recommended for internal variability studies. Several aspects of the control simulation’s mean climate and variability are evaluated against the observational or reanalysis data. The strengths and weaknesses of the control simulation are evaluated. The mean atmospheric circulation is well simulated, except in high latitudes. The Asian-Australian monsoonal meridional cell shows realistic features, however, an artificial rainfall center is located to the eastern periphery of the Tibetan Plateau persists throughout the year. The mean bias of SST resembles that of the standard version, appearing as a “double ITCZ” (Inter-Tropical Convergence Zone) associated with a westward extension of the equatorial eastern Pacific cold tongue. The sea ice extent is acceptable but has a higher concentration. The strength of Atlantic meridional overturning is 27.5 Sv. Evidence from the 600-year simulation suggests a modulation of internal variability on ENSO frequency, since both regular and irregular oscillations of ENSO are found during the different time periods of the long-term simulation. [ABSTRACT FROM AUTHOR]

The capabilities of two versions of the Global–Ocean–Atmosphere–Land–System model (i.e. GOALS–2 and GOALS–4) developed at State Key Laboratory of Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), are validated in terms of the simulations of the winter North Atlantic Oscillation (NAO), which is currently the subject of considerable scientific interest. The results show that both GOALS–2 and GOALS–4 exhibit a realistic NAO signal associated with relatively reasonable spatial patterns of sea level pressure, surface air temperature, and precipitation. Generally speaking, the associated patterns of precipitation in GOALSs match better with the observation in comparison with the case of surface temperature. For the imprint of NAO on the ocean, or perhaps a coupling between the two fluids, the associated tripole patterns of the North Atlantic SST anomaly are presented distinctly in GOALS–2, for GOALS-4 however, this is not the case. Spatially, the models’ main deficiencies appear to be that the simulated Icelandic lows shift northward apparently, which in turn result in the blemish of GOALSs in reproducing the accompanied surface wind anomalies. For the interannual and even longer time scale variations of DJF sea level pressure (SLP) over the North Atlantic region, GOALSs reproduce the center with the strongest variability rationally, but the intensities are far weaker than the observation. [ABSTRACT FROM AUTHOR]