Atmospheric Model Intercomparison Project Research Articles

Internal atmospheric variability of net surface heat flux in reanalyses and CMIP5 AMIP simulations

AbstractThe internal atmospheric variability (IAV) of the net surface heat flux (NHF) in the observed 20th/21st century atmosphere is estimated as the residual after removing the sea surface temperature (SST) and externally forced atmospheric response derived from the four atmospheric models of the Atmospheric Model Intercomparison Project (AMIP) simulations under phase 5 of the Coupled Model Intercomparison Project (CMIP5). The mean NHF of four atmospheric reanalysis datasets is an estimate of the observed NHF. Although the AMIP models are forced with the same SST and external forcing, the forced responses differ significantly among AMIP models, suggestive of uncertainty in the models. Besides, the uncertainty of IAV in the reanalyses could also arise from the uncertainty in reanalyses as observations contain errors and reanalysis includes interpolation by models. It is concluded that: (a) The SST/NHF and SST/forced NHF correlations are significantly negative over most of world ocean in the AMIP models, indicating damping of the SST anomalies by the NHF. (b) The IAV of the AMIP models is not correlated with SST, while the positive IAV/SST correlations in the reanalyses suggests the role of IAV in forcing the SST variability in the extra‐tropics. (c) The standard deviation (STD) of the IAV of AMIP models is indistinguishable from that of the mean reanalysis over a majority of world ocean, and the STD of the NHF of the AMIP models is larger than that of the mean reanalysis in the subtropics and midlatitudes. (d) The IAV in the mean reanalysis plays a role in forcing the SST variability in the extra‐tropics (e.g., Atlantic Multidecadal Variability), while it may not be an important forcing in the tropical oceans (e.g., ENSO).

Observed Statistical Connections Overestimate the Causal Effects of Arctic Sea Ice Changes on Midlatitude Winter Climate

AbstractDisentangling the contribution of changing Arctic sea ice to midlatitude winter climate variability remains challenging because of the large internal climate variability in midlatitudes, difficulties separating cause from effect, methodological differences, and uncertainty around whether models adequately simulate connections between Arctic sea ice and midlatitude climate. We use regression analysis to quantify the links between Arctic sea ice and midlatitude winter climate in observations and large initial-condition ensembles of multiple climate models, in both coupled configurations and so-called Atmospheric Model Intercomparison Project (AMIP) configurations, where observed sea ice and/or sea surface temperatures are prescribed. The coupled models capture the observed links in interannual variability between winter Barents–Kara sea ice and Eurasian surface temperature, and between winter Chukchi–Bering sea ice and North American surface temperature. The coupled models also capture the delayed connection between reduced November–December Barents–Kara sea ice, a weakened winter stratospheric polar vortex, and a shift toward the negative phase of the North Atlantic Oscillation in late winter, although this downward impact is weaker than observed. The strength and sign of the connections both vary considerably between individual 35-yr-long ensemble members, highlighting the need for large ensembles to separate robust connections from internal variability. All the aforementioned links are either absent or are substantially weaker in the AMIP experiments prescribed with only observed sea ice variability. We conclude that the causal effects of sea ice variability on midlatitude winter climate are much weaker than suggested by statistical associations, evident in observations and coupled models, because the statistics are inflated by the effects of atmospheric circulation variability on sea ice.

Tropospheric ozone in CMIP6 simulations

Abstract. The evolution of tropospheric ozone from 1850 to 2100 has been studied using data from Phase 6 of the Coupled Model Intercomparison Project (CMIP6). We evaluate long-term changes using coupled atmosphere–ocean chemistry–climate models, focusing on the CMIP Historical and ScenarioMIP ssp370 experiments, for which detailed tropospheric-ozone diagnostics were archived. The model ensemble has been evaluated against a suite of surface, sonde and satellite observations of the past several decades and found to reproduce well the salient spatial, seasonal and decadal variability and trends. The multi-model mean tropospheric-ozone burden increases from 247 ± 36 Tg in 1850 to a mean value of 356 ± 31 Tg for the period 2005–2014, an increase of 44 %. Modelled present-day values agree well with previous determinations (ACCENT: 336 ± 27 Tg; Atmospheric Chemistry and Climate Model Intercomparison Project, ACCMIP: 337 ± 23 Tg; Tropospheric Ozone Assessment Report, TOAR: 340 ± 34 Tg). In the ssp370 experiments, the ozone burden increases to 416 ± 35 Tg by 2100. The ozone budget has been examined over the same period using lumped ozone production (PO3) and loss (LO3) diagnostics. Both ozone production and chemical loss terms increase steadily over the period 1850 to 2100, with net chemical production (PO3-LO3) reaching a maximum around the year 2000. The residual term, which contains contributions from stratosphere–troposphere transport reaches a minimum around the same time before recovering in the 21st century, while dry deposition increases steadily over the period 1850–2100. Differences between the model residual terms are explained in terms of variation in tropopause height and stratospheric ozone burden.

Tropical Cyclone Characteristics in the MERRA‐2 Reanalysis and AMIP Simulations

AbstractThis study evaluates the tropical cyclone (TC) activity in two high‐resolution data sets—MERRA‐2 Reanalysis (Modern‐Era Retrospective Analysis for Research and Applications, Version 2) and MERRA‐2 AMIP (Atmospheric Model Intercomparison Project). These data sets use the same atmospheric model, the Goddard Earth Observing System Model, Version 5 (GEOS‐5) during the same period. However, while MERRA‐2 AMIP is a free‐running atmospheric simulation forced only with sea surface temperature (SST), MERRA‐2 Reanalysis uses an advanced data assimilation system to include a large variety of data sets. Thus, we analyze (1) the sensitivity of TC activity to the model forcing, (2) how well the TCs in both data sets replicate observed TC characteristics, (3) the sensitivity of these results to tracking schemes and thresholds. Standard diagnostics such as the number of tropical cyclones and their intensity distribution are very similar in the AMIP model and the reanalysis. TCs in both data sets are weaker than observed, as is typical for the spatial resolution of these global models. Overall, the use of data assimilation in the MERRA‐2 Reanalysis does not lead to a significantly better TC climatology than in AMIP. Furthermore, comparison of the MERRA‐2 Reanalysis to two other reanalysis data sets shows that MERRA‐2 generates fewer, but more intense TCs, than those reanalysis products.

Quantifying the Tropical Upper Tropospheric Warming Amplification Using Radio Occultation Measurements

AbstractIn the upper troposphere (UT), Global Navigation Satellite System (GNSS) Radio Occultations (ROs) provide accurate air temperatures (<0.5 K) every ∼200 m vertically. We use RO observations from 01/2002 until 12/2018 at 300, 250, and 200 hPa to quantify the tropical upper tropospheric amplification (defined as the ratio of temperature trends in the UT relative to the surface). We compare the RO‐derived results against Atmospheric Infrared Sounder (AIRS), Coupled Model Intercomparison Project Phase 6 (CMIP6) Atmospheric Model Intercomparison Project (AMIP) models, Modern‐Era Retrospective Analysis for Research and Applications version 2 (MERRA‐2), and European Center for Medium‐range Weather Forecasts Re‐Analysis Interim (ERA‐Interim) data. We find that CMIP6 AMIP models show excellent agreement with independent AIRS and RO observations on the magnitude of the UT warming and show warming that is significantly faster than both reanalyses above 250 hPa. Additionally, AIRS and CMIP6 AMIP present excellent agreement with the RO‐measured tropical tropospheric amplification.

Evidence of nonlinear Walker circulation feedbacks on extreme El Niño Pacific diversity: Observations and CMIP5 models

AbstractThe Walker circulation (WC) is essential for the formation and diversity of El Niño events. However, the nonlinear WC feedback during extreme Central and Eastern El Niño episodes (C and E episodes, respectively) has received little attention. This study used observational datasets and the Atmospheric Model Intercomparison Project (AMIP) and historical simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Eight out of 21 historical models that simulate the El Niño‐Southern Oscillation (ENSO) nonlinearity also simulate the nonlinear Bjerknes feedback in C and E episodes. The opposite does not necessarily occur. However, the underestimation of E might limit the empirical determination. Moreover, few historical models simulate the shallow conditional instability of the second kind (CISK) mechanism. Positive C episodes feature an eastward shift in the ascending branch of the Pacific Walker cell (PWC), while shallow convection prevails over the far‐eastern Pacific (FEP). Positive E events feature two anomalous ascending branches located over the central‐western Pacific (170°W) and FEP (80°W). Positive anomalies in sea surface temperature over the FEP induce the second ascending branch. The positive stratification anomaly in the central Pacific Ocean, which is associated with overestimated Ekman feedback, limits the eastward displacement of the first ascending branch of the PWC. The net surface heat flux determines the duration of growth of the two ascending branches of the PWC during C and E events. Because of their coarse resolution, the historical models underestimate the positive stratification anomaly in the FEP, causing the quick demise of the second ascending branch.

Intermodel Diversity of Simulated Long-term Changes in the Austral Winter Southern Annular Mode: Role of the Southern Ocean Dipole

The Southern Annular Mode (SAM) plays an important role in regulating Southern Hemisphere extratropical circulation. State-of-the-art models exhibit intermodel spread in simulating long-term changes in the SAM. Results from Atmospheric Model Intercomparison Project (AMIP) experiments from 28 models archived in CMIP5 show that the intermodel spread in the linear trend in the austral winter (June−July−August) SAM is significant, with an intermodel standard deviation of 0.28 (10 yr)−1, larger than the multimodel ensemble mean of 0.18 (10 yr)−1. This study explores potential factors underlying the model difference from the aspect of extratropical sea surface temperature (SST). Extratropical SST anomalies related to the SAM exhibit a dipole-like structure between middle and high latitudes, referred to as the Southern Ocean Dipole (SOD). The role of SOD-like SST anomalies in influencing the SAM is found in the AMIP simulations. Model performance in simulating the SAM trend is linked with model skill in reflecting the SOD−SAM relationship. Models with stronger linkage between the SOD and the SAM tend to simulate a stronger SAM trend. The explained variance is about 40% in the AMIP runs. These results suggest improved simulation of the SOD−SAM relationship may help reproduce long-term changes in the SAM.

Tropospheric Ozone Assessment Report

Our understanding of the processes that control the burden and budget of tropospheric ozone has changed dramatically over the last 60 years. Models are the key tools used to understand these changes, and these underscore that there are many processes important in controlling the tropospheric ozone budget. In this critical review, we assess our evolving understanding of these processes, both physical and chemical. We review model simulations from the International Global Atmospheric Chemistry Atmospheric Chemistry and Climate Model Intercomparison Project and Chemistry Climate Modelling Initiative to assess the changes in the tropospheric ozone burden and its budget from 1850 to 2010. Analysis of these data indicates that there has been significant growth in the ozone burden from 1850 to 2000 (approximately 43 ± 9%) but smaller growth between 1960 and 2000 (approximately 16 ± 10%) and that the models simulate burdens of ozone well within recent satellite estimates. The Chemistry Climate Modelling Initiative model ozone budgets indicate that the net chemical production of ozone in the troposphere plateaued in the 1990s and has not changed since then inspite of increases in the burden. There has been a shift in net ozone production in the troposphere being greatest in the northern mid and high latitudes to the northern tropics, driven by the regional evolution of precursor emissions. An analysis of the evolution of tropospheric ozone through the 21st century, as simulated by Climate Model Intercomparison Project Phase 5 models, reveals a large source of uncertainty associated with models themselves (i.e., in the way that they simulate the chemical and physical processes that control tropospheric ozone). This structural uncertainty is greatest in the near term (two to three decades), but emissions scenarios dominate uncertainty in the longer term (2050–2100) evolution of tropospheric ozone. This intrinsic model uncertainty prevents robust predictions of near-term changes in the tropospheric ozone burden, and we review how progress can be made to reduce this limitation.

Origins of the IOD-like Biases in CMIP Multimodel Ensembles: The Atmospheric Component and Ocean–Atmosphere Coupling

AbstractPrevious studies reveal that the last generation of coupled general circulation models (CGCMs) commonly suffer from the so-called Indian Ocean dipole (IOD)-like biases, lowering the models’ ability in climate prediction and projection. The present study shows that such IOD-like biases are reduced insignificantly or even worsen in CGCMs from phase 5 to phase 6 of the Coupled Model Intercomparison Project (CMIP). The origins of the IOD-like biases in CGCMs are further investigated by comparing model outputs from CMIP and the Atmospheric Model Intercomparison Project (AMIP). The CGCMs’ errors are divided into the biases from the AMIP simulation (AMIP biases) and ocean–atmosphere coupling (coupling biases). For the multimodel ensemble mean, the AMIP (coupling) biases account for about two-thirds (one-third) of the IOD-like CMIP biases. In AMIP simulations, the South Asian summer monsoon (SASM) is overly strong; therefore, it could advect overly large easterly momentum from the south Indian Ocean (IO) to the equator. The resultant equatorial easterly wind bias would initiate the convection–circulation feedback and develop large IOD-like AMIP biases. In contrast, the coupling biases weaken the SASM and hence generate warm SST error over the western IO during boreal summer. Such SST error persists to boreal autumn and triggers the Bjerknes feedback, developing the IOD-like coupling biases. Furthermore, the intermodel spread in the IOD-like CMIP biases is largely explained by the intermodel differences in the coupling biases rather than the AMIP biases. The results imply that substantial efforts should be respectively made on reducing the atmospheric models’ intrinsic monsoon biases as well as advancing the simulations of ocean–atmosphere coupling processes.

Evaluation of climate simulations produced with the Brazilian global atmospheric model version 1.2

This paper presents an evaluation of climate simulations produced by the Brazilian Global Atmospheric Model version 1.2 (BAM-1.2) of the Center for Weather Forecast and Climate Studies (CPTEC). The model was run over the 1975–2017 period at two spatial resolutions, corresponding to ~ 180 and ~ 100 km, both with 42 vertical levels, following most of the Atmospheric Model Intercomparison Project (AMIP) protocol. In this protocol, observed sea surface temperatures (SSTs) are used as boundary conditions for the atmospheric model. Four ensemble members were run for each of the two resolutions. A series of diagnostics was computed for assessing the model’s ability to represent the top of the atmosphere (TOA) radiation, atmospheric temperature, circulation and precipitation climatological features. The representation of precipitation interannual variability, El Nino-Southern Oscillation (ENSO) precipitation teleconnections, the Madden and Julian Oscillation (MJO) and daily precipitation characteristics was also assessed. The model at both resolutions reproduced many observed temperature, atmospheric circulation and precipitation climatological features, despite several identified biases. The model atmosphere was found to be more transparent than the observations, leading to misrepresentation of cloud-radiation interactions. The net cloud radiative forcing, which produces a cooling effect on the global mean climate at the TOA, was well represented by the model. This was found to be due to the compensation between both weaker longwave cloud radiative forcing (LWCRF) and shortwave cloud radiative forcing (SWCRF) in the model compared to the observations. The model capability to represent inter-annual precipitation variability at both resolutions was found to be linked to the adequate representation of ENSO teleconnections. However, the model produced weaker than observed convective activity associated with the MJO. Light daily precipitation over the southeast of South America and other climatologically similar regions was diagnosed to be overestimated, and heavy daily precipitation underestimated by the model. Increasing spatial resolution helped to slightly reduce some of the diagnosed biases. The performed evaluation identified model aspects that need to be improved. These include the representation of polar continental surface and sea ice albedo, stratospheric ozone, low marine clouds, and daily precipitation features, which were found to be larger and last longer than the observed features.

Testing a Physical Hypothesis for the Relationship Between Climate Sensitivity and Double‐ITCZ Bias in Climate Models

AbstractTian (2015, https://doi.org/10.1002/2015GL064119) found that Coupled Model Intercomparison Project Phases 3 and 5 (CMIP3 and CMIP5) climate models with too much precipitation in a region of the Southeast Pacific (due to a double‐Intertropical Convergence Zone [ITCZ] bias) tend to have lower climate sensitivities and suggested that this might form the basis of an “emergent constraint,” which could rule out lower values of climate sensitivity. However, no physical mechanism has been proposed to explain this relationship. Here we advance the hypothesis that deep convection encroaching into regions that should be dominated by shallow clouds hampers the formation of shallow clouds in the present climate and reduces the magnitude of positive low‐level cloud feedbacks, resulting in smaller values of climate sensitivity. We test this hypothesis first by performing sensitivity tests with the HadGEM2‐A aquaplanet model subject to a uniform +4 K sea surface temperature (SST) perturbation, in which we vary the degree to which deep convection associated with the single/double ITCZ extends toward subtropical low‐cloud regions. Experiments with more precipitation encroaching into the subtropics have weaker subtropical cloud radiative effects in the present‐day simulations and less positive subtropical cloud feedbacks, consistent with our hypothesis. We test this hypothesis further by looking for the predicted relationships across multimodel ensembles of SST forced Atmospheric Model Intercomparison Project (AMIP) experiments subject to a uniform +4 K SST increase. Relationships of the expected sign are found in the CMIP5 AMIP+4K experiments, but not all are statistically significant at the 5% level. We find no statistically significant support for our hypothesis in the currently available CMIP6 AMIP+4K experiments.

Understanding the bias in surface latent and sensible heat fluxes in contemporary AGCMs over tropical oceans

The performance of 20 models participating in the atmospheric model intercomparison project (AMIP) is evaluated concerning surface latent (QLH) and sensible (QSH) heat flux over the tropical oceans (30°S–30°N). Biases were calculated by comparing model fluxes to observations from moored buoys and the objectively analyzed air–sea fluxes (OAFlux) database. All 20 AMIP models overestimate QLH with an ensemble mean bias of 20 W m−2, and 18 of the 20 models overestimate QSH with an ensemble mean bias of 5 W m−2 when compared to OAFlux, implying a systematic positive bias over the tropical oceans. A comparison with buoy observations also showed similar biases. To obtain insights into the causes behind model bias, we quantified the contribution from near-surface winds, specific humidity, and temperatures. It is found that near-surface humidity contributes more to the bias in QLH than wind speed, while air temperature contributes more to bias in QSH than wind speed. On the other hand, the root mean squared error (RMSE) in QLH has contributions from both near-surface humidity and wind. The contribution from humidity to the mean bias in QLH is 13 W m−2, with RMSE of 15 W m−2, suggesting a systematic overestimation of sea-air humidity difference in models. The model ensemble, in general, simulates QLH and QSH better than individual models. Models with higher horizontal and vertical resolutions perform better than coarse resolution models.

Representation of the boreal summer tropical Atlantic–western North Pacific teleconnection in AGCMs: comparison of CMIP5 and CMIP6

Previous studies have revealed that warm (cold) sea surface temperature (SST) anomalies in the northern tropical Atlantic (NTA) can enhance (weaken) the anomalous low-level anticyclone over the western North Pacific (WNP) during boreal summer. This study assesses the ability of current atmospheric general circulation models (AGCMs) to simulate such an NTA–WNP connection by using Atmospheric Model Intercomparison Project experiments from 23 Coupled Model Intercomparison Project Phase 5 (CMIP5) and 35 CMIP6 climate models. It is shown that both the CMIP5 and CMIP6 multimodel ensemble (MME) averages and the majority of the individual AGCMs can reasonably reproduce the observed pattern of the NTA-related anomalous anticyclone over the WNP during boreal summer. Overall, the performance of the CMIP6 AGCMs in representing the NTA–WNP connection is similar to that of the CMIP5 AGCMs, except that the former tends to have a smaller spread than the latter among models. Additionally, both the CMIP5 and CMIP6 MME averages as well as the individual models can reasonably represent the mechanism responsible for the boreal summer NTA–WNP connection, which involves a zonally westward-extending overturning circulation over the Pacific–Atlantic Oceans. Furthermore, the intensity of the NTA-related WNP anomalous anticyclone is positively correlated with that of the WNP local climatological convection activity for both the CMIP5 and CMIP6 AGCMs, implying that better representation of the WNP climatological convection activity may be crucial for improving the skill of AGCMs to simulate the boreal summer NTA–WNP connection. However, model bias in the simulation of climatological convection activity over the WNP remains large for the current CMIP6 AGCMs, although the bias is reduced over most of the tropical and subtropical Pacific–Atlantic regions compared to that for the CMIP5 AGCMs during boreal summer.

Influences of North Pacific Ocean Domain Extent on the Western U.S. Winter Hydroclimatology in Variable‐Resolution CESM

AbstractVariable‐resolution global climate models (VRGCMs) are a dynamical downscaling method that can reach spatiotemporal scales needed for regional climate assessments. Over the years, several users of VRGCMs have assumed where the location and extent of the refinement domain should be based on knowledge of the prevailing storm tracks and resolution dependence of important regional climate processes (e.g., atmospheric rivers [ARs] and orographic uplift), but the effect of high‐resolution domain size and extent on the simulation of downstream hydroclimatic phenomena has not been systematically evaluated. Here, we use variable resolution in the Community Earth System Model (VR‐CESM) to perform such a test. To do this, three VR‐CESM grids were generated that span the entire, two thirds, and one third of the North Pacific and evaluated for a 30‐year climatology using Atmospheric Model Intercomparison Project protocols. Simulations are compared with reanalysis products offshore (fifth‐generation of the European Centre for Medium‐Range Weather Forecasts atmospheric reanalysis [ERA5]) and onshore (Livneh, 2015, https://doi.org/10.1038/sdata.2015.42, and Parameter‐elevation Regressions on Independent Slopes Model [PRISM]) of the western United States. The westward expansion of refinement domain influenced integrated vapor transport (IVT), which was generally high biased but minimally impacted AR characteristics. Due to slight differences in landfalling AR counts in the western United States, California winter precipitation generally improved with westward expansion of the refinement domain. Western U.S. mountain snowpack and surface temperatures were insensitive to refinement domain size and were more influenced by changes in topographic resolution and/or land surface model version. Given minimal dependence of simulated western U.S. hydroclimate on refinement domain size over the North Pacific, we advise future VR‐CESM studies to focus grid resolution on better resolving land surface heterogeneity.

Assessing Global and Local Radiative Feedbacks Based on AGCM Simulations for 1980–2014/2017

AbstractWe examine radiative feedbacks based on short‐term climate variability by analyzing atmospheric general circulation model (AGCM) simulations, including Atmospheric Model Intercomparison Project within CMIP phase 6 (AMIP6) with known effective radiative forcing (ERF) for 1980–2014 and one with zero ERF for 1980–2017. We first verify the Kernel‐Gregory feedback calculation by showing that both clear‐sky radiative fluxes and all‐sky radiative feedbacks from the kernel method agree with model simulations. We find that global‐mean net feedback for 1980–2017/2014 is −2 W m−2 K−1, about twice the feedback estimated for long‐term warming (4 × CO2) experiments. This difference is mainly caused by a near‐zero global‐mean net cloud feedback for 1980–2017/2014. We show that the lapse rate feedback for 1980–2017/2014 is the largest contributor to the amplified temperature change over the three poles (Arctic, Antarctic, and Tibetan Plateau), followed by surface albedo feedback and Planck feedback deviation from its global mean. Except for a higher surface albedo feedback in Antarctic, other feedbacks are similar between Arctic and Antarctic.

Responses of Cloud-Radiative Forcing to Strong El Niño Events over the Western Pacific Warm Pool as Simulated by CAMS-CSM

Cloud-radiative forcing (CRF) at the top of the atmosphere (TOA) over the western Pacific warm pool (WP) shows unique characteristics in response to El Nino events. In this region, the responses of CRF to El Nino events have been a useful metric for evaluating climate models. Satellite data are used to analyze the CRF anomalies to El Nino events simulated by the new and old versions of the Climate System Model of the Chinese Academy of Meteorological Sciences (CAMS-CSM), which has participated in the Atmospheric Model Intercomparison Project (AMIP). Here, simulations for super El Nino years, El Nino years, and normal years are compared with observations. The results show that the mean values of both longwave CRF (LWCRF) and shortwave CRF (SWCRF) in CAMS-CSM are weaker than the observations for each category of El Nino events. Compared with the old version of CAMS-CSM, the decrease in LWCRF during El Nino events is well simulated by the new version of CAMS-CSM. However, both new and old models cannot reproduce the anomalous SWCRF in El Nino events. The biases in the CRF response to El Nino events are attributed to the biases in the cloud vertical structure because of a weaker crash of the Walker circulation in CAMS-CSM. Due to the modification of the conversion rate from cloud droplets to raindrops in the cumulus convection scheme, the new version of CAMS-CSM has better CRF skills in normal years, but biases in El Nino events still exist in the new version. Improving the response of the Walker circulation to El Nino events is key to higher skills in simulating the cloud radiative responses.

Obtaining More Information about Precipitation Biases over East Asia from Hourly-Scale Evaluation of Model Simulation

The hourly summer precipitation simulations over East Asia by the Chinese Academy of Meteorological Science Climate System Model (CAMS-CSM) high-resolution Atmospheric Model Intercomparison Project (AMIP) runs (T255, ~50 km) were evaluated based on the merged hourly precipitation product released by the China Meteorological Administration (CMA). The results show that the simulation biases are closely related to the topography, with the precipitation amount and frequency overestimated (underestimated), and duration of precipitation events being longer (shorter), over the western high-altitude (eastern plain) regions of China. Six regions with large discrepancies were further analyzed. In terms of the frequency-intensity structure, the overestimation of precipitation frequency is mainly due to the excessive simulated weak precipitation over the four regions with positive biases: the southern edge of the Tibetan Plateau (STP), the northeastern edge of the Tibetan Plateau (NETP), the eastern periphery of the Tibetan Plateau (EPTP), and the mountainous area of North China (NCM); while the underestimation of frequency is mainly due to the insufficient precipitation with moderate intensity over the two regions with negative biases: lower reaches of the Yangtze River (LYR) and the South China coast (SCC). Based on the duration-diurnal structure analysis, two kinds of precipitation events with different natures can be distinguished. The long-duration night to early morning precipitation events have a significant contribution to the precipitation amount biases for all the six key regions, and this kind of precipitation mainly affects the precipitation diurnal variation over the mountainous areas or steep terrain. Although the short-duration afternoon precipitation events only have a greater contribution to the precipitation amount biases over the SCC region, this kind of precipitation affects the diurnal variation over the NCM region and the two key regions with negative biases. Such a detailed hourly-scale evaluation is helpful for enriching the understanding of simulation biases and to further improve model performance.

Effects of Horizontal Resolution on Hourly Precipitation in AGCM Simulations

AbstractTo analyze the effects of horizontal resolution on hourly precipitation, four Atmospheric Model Intercomparison Project simulations are carried out using the Chinese Academy of Sciences Earth System Model (CAS-ESM) and the Community Earth System Model (CESM) during 1998–2016. They include CAS-ESM at resolutions of 1.4° latitude × 1.4° longitude (CAS-ESM L) and 0.5° × 0.5° (CAS-ESM H), and CESM at resolutions of 1.9° latitude × 2.5° longitude (CESM L) and 0.47° × 0.63° (CESM H), respectively. We focus on the simulated hourly precipitation frequency and assess the frequency with respect to high-resolution satellite observations and reanalysis. The high-resolution experiments show some improvements of measurable precipitation (>0.02 mm h−1) frequency. Noticeable improvement of heavy rainfall (>2 mm h−1) frequency is demonstrated at the high resolutions. The zonal mean, seasonal mean, and area-weighted average frequency support the above results. The high-resolution experiments outperform the low-resolution experiments in reproducing hourly precipitation intensity and amount. The added value is apparent in heavy precipitation intensity from CAS-ESM H and CESM H. Over the monsoon regions and tropical convergence zones, the patterns of probability density functions for precipitation from high-resolution experiments are closer to the observations and reanalysis than those from the low-resolution simulations. The improvement of measurable precipitation frequency is mainly caused by the reductions of the convective rainfall occurrence at high resolutions. The increasing large-scale precipitation and reasonable integrated water vapor flux contribute to the improvements in measurable rainfall intensity and heavy precipitation characteristics. The results of this study support the concept that high-resolution global simulations could produce improved hourly precipitation capabilities, especially for heavy rainfall.

Data assimilation using an ensemble of models: a hierarchical approach

Abstract. One characteristic of biogeochemical models is uncertainty about their formulation. Data assimilation should take this uncertainty into account. A common approach is to use an ensemble of models. We must assign probabilities not only to the parameters of the models but also to the models themselves. The method of hierarchical modelling allows us to calculate these probabilities. This paper describes the approach, develops the algebra for the most common case and then applies it to the Atmospheric Tracer Transport Model Intercomparison Project (TransCom). We see that the discrimination among models is unrealistically strong, due to optimistic assumptions inherent in the underlying inversion. The weighted ensemble means and variances from the hierarchical approach are quite similar to the conventional values because the best model in the ensemble is also quite close to the ensemble mean. The approach can also be used for cross-validation in which some data are held back to test estimates obtained with the rest. We demonstrate this with a test of the TransCom inversions holding back the airborne data. We see a slight decrease in the tropical sink and a notably different preferred order of models.

Evaluation of Evaporation Climatology for the Congo Basin Wet Seasons in 11 Global Climate Models

AbstractAcross the Congo, there is a wide spread in rainfall in the two wet seasons in Coupled Model Intercomparison Project 5 global climate models (GCMs). As the Congo is believed to be a moisture recycling hot spot, the evaporation of excess water from the land surface in some models could be amplifying the model spread in rainfall. This study performs an exploratory process‐based evaluation of Congo Basin evaporation in 11 Coupled Model Intercomparison Project 5 GCMs that took part in the Atmospheric Model Intercomparison Project. Our aims are to improve scientific understanding about Congo evaporation, and to determine whether there are opportunities to improve how models produce Congo evaporation. Climatologically, we find that models with “realistic” rainfall simulate higher rainfall in November, the peak of the second wet season, than March, the peak of the first. However, models with “realistic” evaporation simulate lower evaporation in November than March, because these models suppress the transpiration component of the evaporation in November relative to March. In both wet seasons, subgrid rainfall schemes make these models simulate a credible ratio of transpiration to canopy evaporation, and cause them to generate evaporation in a more realistic manner. We therefore trust how these models produce evaporation in the wet seasons, and argue that lower transpiration is likely to explain why evaporation is lower in November than March in reality. We also suggest that using subgrid rainfall schemes in all GCMs could improve how models produce Congo evaporation during the wet seasons. This might reduce the model spread in Congo rainfall.