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This study simulates an unusual extreme rainfall event that occurred in Salvador city, Bahia, Brazil, on December 9, 2017, which was named subtropical storm Guará and had precipitation of approximately 24 mm within less than 1 h. Numerical simulations were conducted using the Weather Research and Forecasting (WRF) model over three domains with horizontal resolutions of 9, 3, and 1 km. Different combinations of seven microphysics, three cumulus, and three planetary boundary layer schemes were evaluated based on their ability to simulate the hourly precipitation during this rainfall event. Statistical indices (MB = –0.69; RMSE = 4.11; MAGE = 1.74; r = 0.55; IOA = 0.66; FAC2 = 0.58) and time series plots showed that the most suitable configurations for this weather event were the Mellor-Yamada-Janjić, Grell-Freitas, and Lin formulations for the planetary boundary layer, cumulus, and microphysics schemes, respectively. The results were compared with the data measured at meteorological stations located in Salvador city. The WRF model simulated well the arrival and occurrence of this extreme weather event in a tropical and coastal region, considering that the region already has intense convective characteristics and is constantly influenced by sea breezes, which could interfere in the model results and compromise the performance of the simulations.

Seasonal forecasts of summer continental United States (CONUS) rainfall have relatively low skill, partly due to a lack of consensus about its sources of predictability. The East Asian monsoon (EAM) can excite a cross-Pacific Rossby wave train, also known as the Asia-North America (ANA) teleconnection. In this study, we analyze the ANA teleconnection in observations and model simulations from the Community Atmospheric Model, version 5 (CAM5), comparing experiments with prescribed climatological SSTs and prescribed observed SSTs. Observations indicate a statistically significant relationship between a strong EAM and increased probability of positive precipitation anomalies over the U.S. west coast and the Plains-Midwest. The ANA teleconnection and CONUS rainfall patterns are improved in the CAM5 experiment with prescribed observed SSTs, suggesting that SST variability is necessary to simulate this teleconnection over CONUS. We find distinct ANA patterns between ENSO phases, with the La Niña-related patterns in CAM5_obsSST disagreeing with observations. Using linear steady-state quasi-geostrophic theory, we conclude that incorrect EAM forcing location greatly contributed to CAM5 biases, and jet stream disparities explained the ENSO-related biases. Finally, we compared EAM forcing experiments with different mean states using a simple dry nonlinear atmospheric general circulation model. Overall, the ANA pattern over CONUS and its modulation by ENSO forcing are well described by dry dynamics on seasonal-to-interannual timescales, including the constructive (destructive) interference between El Niño (La Niña) modulation and the ANA patterns over CONUS.

To improve the ensemble prediction system of the tropical regional atmosphere model for the South China Sea (TREPS) in predicting landfalling tropical cyclones (TCs), the impacts of three new implementing strategies for surface and model physics perturbations in TREPS were evaluated for 19 TCs making landfall in China during 2014–16. For sea surface temperature (SST) perturbations, spatially uncorrelated random perturbations were replaced with spatially correlated ones. The multiplier f, which is used to form perturbed tendency in the Stochastically Perturbed Parameterization Tendency (SPPT) scheme, was inflated in regions with evident convective activity (f-inflated SPPT). Lastly, the Stochastically Perturbed Parameterization (SPP) scheme with 14 perturbed parameters selected from the planetary boundary layer, surface layer, microphysics, and cumulus convection parameterizations was added. Overall, all these methods improved forecasts more significantly for non-intensifying than intensifying TCs. Compared with f-inflated SPPT, the spatially correlated SST perturbations generally showed comparable performance but were more (less) skillful for intensifying (non-intensifying) TCs. The advantages of the spatially correlated SST perturbations and f-inflated SPPT were mainly present in the deterministic guidance for both TC track and wind and in the probabilistic guidance for reliability of wind. For intensifying TCs, adding SPP led to mixed impacts with significant improvements in probability-matched mean of modest winds and in probabilistic forecasts of rainfall; while for non-intensifying TCs, adding SPP frequently led to positive impacts on the deterministic guidance for track, intensity, strong winds, and moderate rainfall and on the probabilistic guidance for wind and discrimination of rainfall.

The Bay of Bengal (BoB) sea surface temperatures (SSTs) are found to be negatively correlated with the Indian summer monsoon (ISM) rainfall at seasonal, monthly and daily time scales. It is intriguing that the ISM seasonal mean rainfall is reported to be decreasing in the recent decades while no prominent warming has been observed over the BoB. We posit that the reason why BoB warming is not apparent is because BoB surface waters stay largely above the convective threshold of ~ 28 °C and further warming may be perceivable only in terms of an atmospheric response. This is consistent with prominent increase in the latent heat flux (LHF) over BoB during the pre-monsoon season. However such an increasing trend in LHF is not seen for the monsoon season. The increased surface evaporation leading to an increase in LHF over BoB enhances convection aloft which drives a stronger low level pre-monsoon circulation. We hypothesize that the dynamic response of the atmosphere associated with the pre-monsoon circulation changes driven by the BoB warming can explain, at least in part, the observed ISM rainfall trend. This hypothesis is tested by diagnosing SST-forced simulations with the Community Atmosphere Model version 6. The results suggest that the BoB warming does generate dynamic and thermodynamic responses which enhance moisture convergence (divergence) and convection over BoB (central India) during the pre-monsoon season. As a consequence, evapotranspiration is enhanced over the Indian subcontinent leaving the ground drier and colder. The net result is a weaker land–ocean thermal contrast and a weaker monsoon circulation and hence reduced seasonal rainfall totals.

Evidence clearly shows that the mid-tropospheric North Atlantic subtropical high (NASH) is much stronger than the North Pacific subtropical high (NPSH) in summer, but the mechanism is unknown. To understand the mechanism for such zonal difference between the two systems, we perform a series of sensitivity simulations using a community atmosphere model (CAM) and a linear baroclinic model (LBM). The CAM results indicate that the zonal variation of tropical sea-surface temperatures (SSTs), followed by Tibetan Plateau (TP), play leading roles in the formation of these mid-level subtropical high differences through modulating precipitation-related heating sources. The impacts of such heat sources are then investigated using the LBM. The tropical SST variation-related convective heating over the northeastern equatorial Pacific acts to enhance the NASH via exciting an eastward-propagating wave pattern resembling the Pacific-North American teleconnection; the TP-induced large-scale latent heating can also cause the NASH-NPSH difference by exciting a circumglobal teleconnection (CGT)-like wave pattern with westward-propagating part and eastward-propagating part along the westerly jet stream. Thus, their combined effect makes the NASH much stronger than the NPSH in the mid troposphere during summertime.

Globally gridded sea surface temperatures (SSTs) provide key data for long-term monitoring of the stability of satellite data. Despite apparent limitations, accurate hyperspectral data can provide useful independent information to critique the stability of global SST products on the annual-to-decadal time scale. We compared data from atmospheric infrared sounder (AIRS) on EOS Aqua and Crosstrack Interferometer Sounder (CrIS) on SNPP with the SST products from NOAA/NESDIS [real time global (RTG)], the Canadian Meteorological Centre (CMC), and the U.K. Met Office [Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA)]. For the 2013–2019 period, the overall standard deviation of the difference between AIRS and the RTG was 0.55 K, with an increasing trend over time. In contrast, the standard deviation of the difference between the AIRS, the CMC, and the OSTIA dropped steadily to below 0.4 K, a level previously seen only in SST products relative to independent buoy data. Unexplained biases between the observed and the gridded SSTs at the 100-mK level are consistent with the already existing estimates of the AIRS and CrIS absolute calibration accuracy. However, the AIRS and CrIS observations both show artifacts in all three SST products, increasing with distance from the equator, with the CMC artifacts being the smallest. Even with the CMC, a trend of 4 mK per year relative to AIRS and CrIS was observed between 2013 and 2019 for the 30S–30N oceans. Investigation of the underlying causes of the observed discrepancies requires further work.

Environment and Climate Change Canada has initiated the production of a 1980–2018, 10 km, North American precipitation and surface reanalysis. ERA-Interim is used to initialize the Global Deterministic Reforecast System (GDRS) at a 39 km resolution. Its output is then dynamically downscaled to 10 km by the Regional Deterministic Reforecast System (RDRS). Coupled with the RDRS, the Canadian Land Data Assimilation System (CaLDAS) and Precipitation Analysis (CaPA) are used to produce surface and precipitation analyses. All systems used are close to operational model versions and configurations. In this study, a 7-year sample of the reanalysis (2011–2017) is evaluated. Verification results show that the skill of the RDRS is stable over time and equivalent to that of the current operational system. The impact of the coupling between RDRS and CaLDAS is explored using an early version of the reanalysis system which was run at 15 km resolution for the period 2010–2014, with and without the use of CaLDAS. Significant improvements are observed with CaLDAS in the lower troposphere and surface layer, especially for the 850 hPa dew point and absolute temperatures in summer. Precipitation is further improved through an offline precipitation analysis which allows the assimilation of additional observations of 24 h precipitation totals. The final dataset should be of particular interest for hydrological applications focusing on transboundary and northern watersheds, where existing products often show discontinuities at the border and assimilate very few – if any – precipitation observations.

Atmospheric transient eddy dynamical forcing (TEDF)-driven midlatitude unstable air–sea interaction has recently been recognized as a crucial positive feedback for the maintenance of the extratropical decadal variabilities. Our recent theoretical work (Chen et al., Clim Dyn 10.1007/s00382-020-05405-0, 2020) has characterized such an interaction through building an analytical midlatitude barotropic atmospheric model coupled to a simplified upper oceanic model. This study extends the analytical model to including a two-layer quasi-geostrophic baroclinic atmospheric model and then identifies the roles of vertical distributions of atmospheric TEDF and diabatic heating in midlatitude unstable air–sea interaction. It is found that midlatitude air–sea coupling with more realistic vertical profiles of atmospheric TEDF and diabatic heating destabilizes oceanic Rossby wave modes over the entire range of zonal wavelengths, in which the most unstable coupled mode features an equivalent barotropic atmospheric low (high) pressure over a cold (warm) oceanic surface. Spatial structure and period of the most unstable mode are more consistent with the observation than those from in previous model. Although either TEDF or diabatic heating alone can lead to a destabilized coupled mode, the former makes a dominant contribution to the instability. The increase of low-layer TEDF stimulates the instability more effectively if the TEDF in upper layer is larger than in lower layer, while the TEDF in either high or low layers can individually cause the instability. The surface heating always destabilizes the air–sea interaction, while the mid-level heating always decays the coupled mode. The results of this study further confirm the TEDF-driven positive feedback mechanism in midlatitude air–sea interaction proposed by recent observational and numerical experiment studies.

High-resolution modeling is implemented to investigate an evolution of two quasi-tropical cyclones (QTC) over the Black Sea: in September of 2005 and 2018. For comparison, the quasi-tropical hurricane Zorba, that developed over the Mediterranean Sea at the end of September 2018, is considered. An assessment of factors leading to the origination and intensification of QTCs is carried out, and the predictability of the phenomenon is studied. The authors tried to reduce the analysis to the parameters obtained directly as a result of mesoscale nonhydrostatic modeling. In all the considered cases, the period of active development of cyclones was preceded by the formation of a core of warm air above the sea surface; the moisture inflow from the lateral boundaries was more important in the moisture balance. It is found that coarser spatial resolution predictions are more successful for large QTCs.

Coastal regions are generally densely populated and have become highly vulnerable to the occurrence of extreme events. In recent years, Brazil’s southern coastal region has been affected by several different extreme weather events that have caused coastal flooding, with economic losses as well as fatalities. Understanding and improving the predictability of these events has become a major issue for the local population. In this study, state-of-the-art numerical modeling was applied to this region to assess the ability of the Ocean-Land Atmosphere Model to represent major extreme events. The model was applied to the region with a high-resolution grid refinement technique capable of simultaneously representing global and local atmospheric phenomena. The main events that affected Brazil’s southern coastal region between 2000 and 2018 were identified and then simulated. All selected events were associated with cyclonic and/or anticyclonic systems near the coastal region of the study area. Those systems were responsible for bringing heavy rain, strong winds and sea level rise, causing impacts for the coastal region. The results of the numerical simulations were compared with observational data to evaluate model performance. The model simulated well the air temperature and wind fields. Correlation values for sea level pressure were high despite a maximum positive bias of approximately 2 hPa. Precipitation presented a negative bias for most events. Finally, the results show that the methodology allowed for a detailed representation of sensible and latent heat fluxes for the region, allowing a better representation of local mesoscale features.

This study assesses the performance of the Weather Research and Forecasting (WRF) model in simulating the Arabian Peninsula summer climate for the period 2001–2016. The European Centre for Medium range Weather Forecast (ECMWF) reanalysis is downscaled using WRF without (CTRL) and with the Spectral Nudging (SPN) method. Our results suggest that the noticeable cold biases in surface temperatures (mean, minimum, and maximum) over the Arabian Peninsula in CTRL are significantly reduced in SPN. The seasonal patterns of surface pressure, cloud cover, lower and upper tropospheric circulation, and mid-tropospheric anticyclone are also simulated more realistically with SPN. The evaluation of mean vertical profiles of dynamical and thermo-dynamical features over the Arabian Peninsula further confirms the enhanced simulations with SPN with respect to CTRL. Though SPN captures better the observed evolution of rainfall compared to that of CTRL, it produces a positive rainfall bias over the Southwestern Arabian Peninsula. Stronger vertical motions associated with the local topography enhance the higher water vapor loading, condenses in the upper layers, and results in excess amount of rainfall in SPN. Furthermore, with SPN, WRF is further able to better simulate the synoptic features of heat waves. Overall, SPN enhances WRF simulation skill of the horizontal structures and vertical profiles of the Arabian Peninsula summer climate by enforcing a better balance between the small and large scale features and associated feedbacks.

The influence of early spring sea ice at Barents Sea on midsummer rainfall in Northeast China (NEC) is identified based on observational analyses and atmospheric modeling experiments in this study. Increased sea ice area (SIA) in the Barents Sea is ensued by positive rainfall anomalies at north of NEC and by negative anomalies at south, and vice verse. Specifically, due to a good seasonal persistence from spring to summer, the preceding sea ice anomalies exert an impact on midsummer surface air temperature anomalies and vertical stability over Barents Sea via the modulation on turbulent heat flux. The anomalous circulation is further triggered over Europe and the Mediterranean Sea through meridional vertical cells, with a barotropic structure. Accordingly, an effective Rossby wave source is excited over the eastern Mediterranean by the advection of vorticity by divergence wind, and causes an eastward propagation of Silk Road Pattern to East Asia. In addition, another SIA-related wave-like train can diffuse directly southeastward from Arctic to NEC in a polar path. Observations and numerical simulations indicate that, in response to increased sea ice at Barents Sea, an anomalous cyclone emerges over NEC, along with easterly or southeasterly over north of NEC and with northwesterly over south, leading to moisture convergence anomalies at north and divergence anomalies at south. Jointly, ascending (descending) motion anomalies favors a wet (dry) summer over north (south) of NEC.

The autumn precipitation over the Sichuan Basin (APSB) experienced a significant wet-to-dry shift around the mid-1980s during the 1961–2010 period. Thus, the dataset is divided into two subperiods, 1961–1985 (P1) and 1986–2010 (P2), which are denoted as the wet period and dry period, respectively. The possible mechanisms accounting for the wet-to-dry shift in the APSB are investigated using both observational data and a baroclinic atmospheric model. Analysis shows that the sea surface temperature (SST) in the tropical Indian-western Pacific (TIWP) regions displays continuous warming during the period under examination, which may intensify the ascent motion over the warm pool and favor the descent motion over the Sichuan Basin. Meanwhile, the Gill-type atmospheric response to anomalous TIWP warm SST is accompanied by anomalous northerly winds prevailing over central and Southwest China. Both the intensified descent motion and anomalous northerly winds over central and Southwest China favor the switch to dry conditions in the APSB. Further analysis shows that the decadal changes in the North Atlantic SST lead to an atmospheric wave pattern prevailing over the extratropical Eurasian continent. An anomalous cyclone over central-eastern Eurasia, which is one component of the wave pattern, is accompanied by significant northerly winds penetrating southward to central China. These northerly winds can cause more-than-normal snow cover around Lake Baikal, which may in turn modulate the atmospheric circulation through positive feedback and therefore result in intensified northerly winds, favorable for the switch to the anomalous dry state of the APSB.

The monsoon-driven river basins are more vulnerable to the flash-floods triggered by intense rainfall activities. Due to the lack of adequate high resolution forecast of real-time hydro-meteorological variables, a reliable forecast of flash floods remains a challenge. One plausible way to generate such a high-resolution forecast of hydro-meteorological variables is to use a coupled atmospheric–hydrologic modelling system. Thus, in this study, a physically-based, fully distributed, multi-scale hydrologic modelling framework, WRF-Hydro with optimized configurations (stand-alone and coupled-mode) is used to simulate the important hydro-meteorological variables like precipitation, runoff, soil moisture, and land surface heat fluxes over Cauvery river basin, India. In stand-alone mode, the model is driven by the high-resolution gridded data from the Global Land Data Assimilation System; while the coupled model is run with the WRF atmospheric model. In this study, the ability of a fully coupled WRF–WRF-Hydro modelling framework , with 3 km grid spacing is used to simulate the hydro-meteorological conditions during an extreme rainfall event (08–09 August 2019). The innermost domain of WRF-Hydro in conjunction with a high resolution hydrological routing grid (300 m) is also utilized, to include the subgrid scale disaggregation–aggregation weighting procedures to generate the land–atmospheric feedbacks on the hydrometeorological variables. The resulting variables have been validated through relevant observations; the overall performance of the coupled WRF-Hydro is shown to be relatively good when compared to the WRF-only simulations.

This article describes the development and ocean forecast evaluation of an atmosphere–ocean coupled prediction system for the Maritime Continent (MC) domain, which includes the eastern Indian and western Pacific oceans. The coupled system comprises regional configurations of the atmospheric model MetUM and ocean model NEMO at a uniform horizontal resolution of 4.5 km × 4.5 km, coupled using the OASIS3-MCT libraries. The coupled model is run as a pre-operational forecast system from 1 to 31 October 2019. Hindcast simulations performed for the period 1 January 2014 to 30 September 2019, using the stand-alone ocean configuration, provided the initial condition to the coupled ocean model. This paper details the evaluations of ocean-only model hindcast and 6 d coupled ocean forecast simulations. Direct comparison of sea surface temperature (SST) and sea surface height (SSH) with analysis, as well as in situ observations, is performed for the ocean-only hindcast evaluation. For the evaluation of coupled ocean model, comparisons of ocean forecast for different forecast lead times with SST analysis and in situ observations of SSH, temperature, and salinity have been performed. Overall, the model forecast deviation of SST, SSH, and subsurface temperature and salinity fields relative to observation is within acceptable error limits of operational forecast models. Typical runtimes of the daily forecast simulations are found to be suitable for the operational forecast applications.

Regional seasonal forecasting requires accurate simulation of the variability of local climate drivers. The South Pacific Convergence Zone (SPCZ) is a large region of low-level convergence, clouds and precipitation in the South Pacific, whose effects extend as far as northeast Australia (NEA). The location of the SPCZ is modulated by the El Niño-Southern Oscillation (ENSO) which causes rainfall variability in the region. Correctly simulating the ENSO-SPCZ teleconnection and its interplay with local conditions is essential for improving seasonal rainfall forecasts. Here we analyse the ability of the ACCESS-S1 seasonal forecast system to predict the SPCZ’s relationship with ENSO including its latitudinal shifts, zonal slope and rainfall magnitude between 1990 and 2012 for the December–January–February (DJF) season. We found improvements in ACCESS-S1’s SPCZ prediction capability compared to its predecessor (POAMA), although prediction of the slope is still limited. The inability of ACCESS-S1 to replicate seasons with a strong anti-zonal SPCZ slope is attributed to its atmospheric model. This has implications for accurate seasonal rainfall forecasts for NEA and South Pacific Islands. Future challenges in seasonal prediction facing regional communities and developers of coupled ocean–atmosphere forecast models are discussed.

Snow depth over sea ice is an essential variable for understanding the Arctic energy budget. In this study, we evaluate snow depth over Arctic sea ice during 1993–2014 simulated by 31 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) against recent satellite retrievals. The CMIP6 models capture some aspects of the observed snow depth climatology and variability. The observed variability lies in the middle of the models’ simulations. All the models show negative trends of snow depth during 1993–2014. However, substantial spatiotemporal discrepancies are identified. Compared to the observation, most models have late seasonal maximum snow depth (by two months), remarkably thinner snow for the seasonal minimum, an incorrect transition from the growth to decay period, and a greatly underestimated interannual variability and thinning trend of snow depth over areas with frequent occurrence of multi-year sea ice. Most models are unable to reproduce the observed snow depth gradient from the Canadian Arctic to the outer areas and the largest thinning rate in the central Arctic. Future projections suggest that snow depth in the Arctic will continue to decrease from 2015 to 2099. Under the SSP5-8.5 scenario, the Arctic will be almost snow-free during the summer and fall and the accumulation of snow starts from January. Further investigation into the possible causes of the issues for the simulated snow depth by some models based on the same family of models suggests that resolution, the inclusion of a high-top atmospheric model, and biogeochemistry processes are important factors for snow depth simulation.

This paper presents an inter-comparison performance assessment of the newly developed Centre for Weather Forecast and Climate Studies (CPTEC) model (the Brazilian Atmospheric Model version 1.2, BAM-1.2) against four sub-seasonal to seasonal (S2S) prediction project models from: Japan Meteorological Agency (JMA), Environmental and Climate Change Canada (ECCC), European Centre for Medium-range Weather Forecasts (ECMWF) and Australian Bureau of Meteorology (BoM). The inter-comparison was performed using hindcasts of weekly precipitation anomalies and the daily evolution of Madden–Julian Oscillation (MJO) for 12 extended austral summers (November–March, 1999/2000–2010/2011), leading to a verification sample of 120 hindcasts. The deterministic assessment of the prediction of precipitation anomalies revealed ECMWF as the model presenting the highest (smallest) correlation (root mean squared error, RMSE) values among all examined models. JMA ranked as the second best performing model, followed by ECCC, CPTEC and BoM. The probabilistic assessment for the event “positive precipitation anomaly” revealed that ECMWF presented better discrimination, reliability and resolution when compared to CPTEC and BoM. However, these three models produced overconfident probabilistic predictions. For MJO predictions, CPTEC crosses the 0.5 bivariate correlation threshold at around 19 days when using the mean of 4 ensemble members, presenting similar performance to BoM, JMA and ECCC. Overall, CPTEC proved to be competitive compared to the S2S models investigated, but with respect to ECMWF there is scope to improve the prediction system, likely by a combination of including coupling to an interactive ocean, improving resolution and model parameterization schemes, and better methods for ensemble generation.

High spatiotemporal resolution land surface temperature (LST) plays an important role in various environment applications. However, the limitation of thermal infrared sensors and the effect of clouds and other atmospheric conditions result in discontinuous daily thermal observations of the Moderate Resolution Imaging Spectroradiometer (MODIS). Annual temperature cycle (ATC) models can help to supply daily continuous LSTs via limited observations, but these ATC models seldom consider the disturbance of weather conditions or the land cover change. On the other hand, spatial interpolation techniques also limit in implementation when available data in one day or several days are not able to obtain enough spatiotemporal information for LST reconstruction. The objective of this study is to propose a phenology-based ATC model (termed PATC), which takes the phenology change and local weather change into account, to reconstruct daily unscanned LSTs at an annual scale. Daily MODIS LSTs collected in 2015 were utilized to analyze the performance of PATC compared with other ATC models. Results show that PATC improved the accuracy by 1.6 and 0.5 K compared to the classic ATC model in the daytime and nighttime, respectively. Compared to the enhanced ATC model, PATC also shows better performance with higher accuracies, especially during the growing season of vegetation in the daytime. Future research may focus on an incorporation with Landsat observations and diurnal temperature cycle models to implement LST reconstruction at a diurnal scale.

The simulation and prediction of winter Northern Hemisphere atmospheric blocking in the seasonal prediction systems from the European Centre for Medium‐Range Weather Forecasts (ECMWF) is analysed. Blocking statistics from the operational November‐initialised seasonal hindcasts are evaluated in three generations of models: System3, System4, and System5 (SEAS5). Improvements in the climatological representation of blocking are observed in the most recent model configurations, with reduced bias over North Pacific and Greenland. Minor progress is seen over the European sector, where SEAS5 still underestimates the observed blocking frequency. SEAS5 blocking interannual variability is underestimated too and is proportional to the climatological frequency, highlighting that a negative bias in the blocking frequency implies an underestimation of the interannual variance. SEAS5 predictive skill and signal‐to‐noise ratio remain low, but interesting positive results are found over Western and Central Europe. Improved forecasts with reduced ensemble spread are obtained during El Niño years, especially at low latitudes. Complementary experiments show that the statistics of blocking are improved following atmospheric and oceanic resolution increase. Conversely, they remain largely insensitive to coupled model sea‐surface temperature (SST) errors. On the other hand, the implementation of stochastic parameterisations tends to displace blocking activity equatorward. Finally, by comparing seasonal hindcasts with climate runs using the same model, we highlight that the largest contributors to the chronic underestimation of blocking are persistent errors in the atmospheric model. It is also shown that SST errors have a larger impact on blocking bias in climate runs than in seasonal runs, and that increased ocean model resolution contributes to improved blocking more effectively in climate runs. Seasonal forecasts can thus be considered a suitable test‐bed for model development targeting blocking improvement in climate models.

The Coupled Model Intercomparison Project Phase 6 (CMIP6) dataset is used to examine projected changes in temperature and precipitation over the United States (U.S.), Central America and the Caribbean. The changes are computed using an ensemble of 31 models for three future time slices (2021–2040, 2041–2060, and 2080–2099) relative to the reference period (1995–2014) under three Shared Socioeconomic Pathways (SSPs; SSP1-2.6, SSP2-4.5, and SSP5-8.5). The CMIP6 ensemble reproduces the observed annual cycle and distribution of mean annual temperature and precipitation with biases between − 0.93 and 1.27 °C and − 37.90 to 58.45%, respectively, for most of the region. However, modeled precipitation is too large over the western and Midwestern U.S. during winter and spring and over the North American monsoon region in summer, while too small over southern Central America. Temperature is projected to increase over the entire domain under all three SSPs, by as much as 6 °C under SSP5-8.5, and with more pronounced increases in the northern latitudes over the regions that receive snow in the present climate. Annual precipitation projections for the end of the twenty-frst century have more uncertainty, as expected, and exhibit a meridional dipole-like pattern, with precipitation increasing by 10–30% over much of the U.S. and decreasing by 10–40% over Central America and the Caribbean, especially over the monsoon region. Seasonally, precipitation over the eastern and central subregions is projected to increase during winter and spring and decrease during summer and autumn. Over the monsoon region and Central America, precipitation is projected to decrease in all seasons except autumn. The analysis was repeated on a subset of 9 models with the best performance in the reference period; however, no signifcant diference was found, suggesting that model bias is not strongly infuencing the projections. Universidad de Costa Rica/[805-B9-454]/UCR/Costa Rica National Science Foundation/[AGS-1849654]/NSF/Estados Unidos National Science Foundation/[AGS-1623912]/NSF/Estados Unidos Department of Energy/[2316‐T849‐08]/DOE/Estados Unidos National Oceanic and Atmospheric Administration/[2316‐T849‐08]/NOAA/Estados Unidos UCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigaciones Geofísicas (CIGEFI) UCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigación en Ciencias del Mar y Limnología (CIMAR)

Forecasts with the European Centre for Medium‐Range Weather Forecasts' numerical weather prediction model are evaluated using an extensive set of observations from the Arctic Ocean 2018 expedition on the Swedish icebreaker Oden. The atmospheric model (Cy45r1) is similar to that used for the ERA5 reanalysis (Cy41r2). The evaluation covers 1 month, with the icebreaker moored to drifting sea ice near the North Pole; a total of 125 forecasts issued four times per day were used. Standard surface observations and 6‐hourly soundings were assimilated to ensure that the initial model error is small. Model errors can be divided into two groups. First, variables related to dynamics feature errors that grow with forecast length; error spread also grows with time. Initial errors are small, facilitating a robust evaluation of the second group; thermodynamic variables. These feature fast error growth for 6–12 hr, after which errors saturates; error spread is roughly constant. Both surface and near‐surface air temperatures are too warm in the model. During the summer both are typically above zero in spite of the ongoing melt; however, the warm bias increases as the surface freezes. The warm bias is due to a too warm atmosphere; errors in surface sensible heat flux transfer additional heat from the atmosphere to the surface. The lower troposphere temperature error has a distinct vertical structure: a substantial warm bias in the lowest few 100 m and a large cold bias around 1 km; this structure features a significant diurnal cycle and is tightly coupled to errors in the modelled clouds. Clouds appear too often and in a too deep layer of the lower atmosphere; the lowest clouds essentially never break up. The largest error in cloud presence is aligned with the largest cold bias at around 1 km.

The study of climate change has become an important topic because of its negative impact on human life. The North-East African part lacks the studies for climate change detection, despite it being one of the most affected parts worldwide. The relationship between the emission of greenhouse gases (GHGs) and climate change is an important factor to understand. To investigate this linkage, we used machine-learning (ML) models based on essential climate variables (ECVs) to investigate the relationship between the GHGs and the rhythm of climate variable change. The article investigates how ML techniques can be applied to climatic data to build an ML model that is able to predict the state of climate variables for the short and long term. By selecting a candidate model, it will help in climate adaptation and mitigation, also determine at what level GHGs should be kept and their corresponding concentrations in order to avoid climate events and crises. The used models are long short-term memory, autoencoders, and convolutional neural network (CNN). Alternatively, the dataset has been selected from U.K. National Centre for Earth Observation and Copernicus Climate Change Services. We compared the performance of these techniques and the best candidate was the Head—CNN; based on performance metrics such as root-mean-squared-error: 5.378, 2.395, and 15.923, mean-absolute-error: 4.157, 1.928, and11.672, Pearson: 0.368, 0.649, and 0.291, and R2 coefficient: 0.607, 0.806, and 0.539 for the ECVs temperature, CO2, and CH4, respectively. We were able to link the GHG emission to ECVs with high accuracy based on the reading of this geographic area.

The Kuroshio Extension (KE) shifts between elongated and convoluted states on interannual to decadal time scales. The nature of this low frequency variability (LFV) is still under debate since it is known to be driven by intrinsic oceanic mechanisms, but it is also synchronized with the Pacific Decadal Oscillation (PDO). In this analysis we present the results from two present-climate coupled simulations performed with the CMCC-CM2 model under the CMIP6 HighResMIP protocol and differing only by their atmospheric component resolution. The impact of increased atmospheric resolution on the KE LFV is assessed inspecting several aspects: the KE bimodality, the large-scale variability and the air–sea interactions. The KE LFV and the teleconnection mechanism that connects the KE and the PDO are well captured by both configurations. However, higher atmospheric resolution favors the occurrence of the elongated state and leads to a more realistic PDO representation. Moreover, both simulations qualitatively capture the signatures of atmosphere-driven and ocean-driven regimes over the North Pacific Ocean, even if the higher resolution induces an excessively strong ocean–atmosphere coupling that leads to an overestimation of the air–sea feedbacks. This work highlights that the small scale atmospheric variability (resolution lower than 1°) does not substantially contribute to improve the realism of the KE LFV, but causes significant differences in the air–sea interaction over the KE region likely related to the strengthening of the coupling. The eddy-permitting ocean resolution shared by both configurations is likely responsible for the degree of realism exhibited by the simulated KE LFV in the two analyzed simulations.

The Antarctic Peninsula region is of growing interest due to the regional climate change features and related atmospheric circulation patterns. The regional mesoscale atmospheric model Polar Weather Research and Forecasting (WRF) v4.1.1 was used in this research to study a heavy precipitation event over the Ukrainian Antarctic Akademik Vernadsky station region (Antarctic Peninsula). The passage of the cyclone over the Antarctic Peninsula as a typical synoptic process as well as a case of the daily precipitation maximum amount of 2018 were chosen for investigation in this research. The estimation of the modelling quality and downscaling was done by comparing the obtained results with in-situ observation at the Akademik Vernadsky station and cross-domain tracking of average meteorological values and their deviation. The concept of the nested domains allowed to increase the horizontal resolution of the simulated atmosphere up to 1 km and to reproduce the wind regime of this region with high quality. Comparison with measured data showed a significant improvement in wind simulation with increasing of resolution, but worse representation of surface temperature and humidity. The Polar WRF made a general cooling of near surface temperature of 2 °C during the period of simulation and increased precipitation amount by 4.6–8.4 mm (12–21%) on average over the territory relative to the initial data from Global Data Assimilation System. This can be explained by the contribution of noise and imperfection of the model (including static input data of the terrain description). Based on the modelled results, the interaction of wind flow with the mountainous terrain of the Antarctic Peninsula creates a range of complex dynamic effects in the atmosphere. These effects cause local precipitation maxima both over the Peninsula and over the adjacent ocean. These are, respectively, bay-valley areas of increased precipitation and increased precipitation on the crests of shock waves from orographic obstacles. Under certain background wind conditions, the influence of the latter effect can reach the Akademik Vernadsky station and cause the formation of heavy precipitation here.

The biophysical effects of reforestation and afforestation (herein jointly called re/afforestation) on the diurnal temperature cycle in European summer are investigated by analyzing a regional climate model (RCM) ensemble, established within the Land Use and Climate Across Scales Flagship Pilot Study (LUCAS FPS). With this RCM ensemble, two idealized experiments are performed for Europe, one with a continent with maximized forest cover, and one in which all forests are turned into grassland. First, an in-depth analysis of one ensemble member (“CCLM-VEG3D”) is carried out, to reveal the complex process chain caused by such land use changes (LUCs). From these findings, the whole ensemble is analyzed and principal biophysical effects of re/afforestation are derived. Results show that the diurnal temperature range is reduced at the surface (top of the vegetation) with re/afforestation. Most RCMs simulate colder surface temperatures Tsurf during the day and warmer Tsurf during the night. Thus, for the first time, the principal temperature interrelations found in observation-based studies in the midlatitudes could be reproduced within a model intercomparison study. On the contrary, the diurnal temperature range in the lowest atmospheric model level (Tair) is increased with re/afforestation. This opposing temperature response is mainly caused by the higher surface roughness of forest, enhancing the turbulent heat exchange. Furthermore, these opposing temperature responses demonstrate that the use of the diagnostic 2-m temperature (weighted interpolation between Tsurf and Tair) has a limited potential to assess the effects of re/afforestation. Thus, studies about the biophysical impacts of LUCs should investigate the whole near-surface temperature profile.

European flood frequency and intensity change on a multidecadal scale. Floods were more frequent in the 19th (Central Europe) and early 20th century (Western Europe) than during the mid-20th century and again more frequent since the 1970s. The causes of this variability are not well understood and the relation to climate change is unclear. Palaeoclimate studies from the northern Alps suggest that past flood-rich periods coincided with cold periods. In contrast, some studies suggest that more floods might occur in a future, warming world. Here we reconcile the apparent contradiction by addressing and quantifying the contribution of atmospheric processes to multidecadal flood variability. For this, we use long series of annual peak streamflow, daily weather data, reanalyses, and reconstructions. We show that both changes in atmospheric circulation and moisture content affected multidecadal changes of annual peak streamflow in Central and Western Europe over the past two centuries. We find that during the 19th and early 20th century, atmospheric circulation changes led to high peak values of moisture flux convergence. The circulation was more conducive to strong and long-lasting precipitation events than in the mid-20th century. These changes are also partly reflected in the seasonal mean circulation and reproduced in atmospheric model simulations, pointing to a possible role of oceanic variability. For the period after 1980, increasing moisture content in a warming atmosphere led to extremely high moisture flux convergence. Thus, the main atmospheric driver of flood variability changed from atmospheric circulation variability to water vapour increase.

Following collapses of the neighbouring Larsen A and B ice shelves, Larsen C has become a focus of increased attention. Determining how the prevailing meteorological conditions influence its surface melt regime is of paramount importance for understanding the dominant processes causing melt and ultimately for predicting its future. To this end, a new, high-resolution (4 km grid spacing) Met Office Unified Model (MetUM) hindcast of atmospheric conditions and surface melt processes over the central Antarctic Peninsula is introduced. The hindcast is capable of simulating observed near-surface meteorology and surface melt conditions over Larsen C. In contrast with previous model simulations, the MetUM captures the observed east-west gradient in surface melting associated with foehn winds, as well as the inter-annual variability in melt shown in previous observational studies. As exemplars, we focus on two case studies – the months preceding the collapse of the Larsen B ice shelf in March 2002 and the high-foehn, high-melt period of March-May 2016 - to test the hindcast’s ability to reproduce the atmospheric effects that contributed to considerable melting during those periods. The results suggest that the MetUM hindcast is a useful tool with which to explore the dominant causes of surface melting on Larsen C.

To evaluate the performance of a high-resolution atmospheric model (HiRAM) and to improve our understanding of the climatic impacts of ENSO forcing and associated teleconnections, we analyzed AMIP style HiRAM simulations conducted effectively at 25 km grid spacing. To better assess HiRAM response to ENSO climate variability; we categorized it into strong and weak El Nino/La Nina episodes. The HiRAM model reproduced the impacts of strong ENSO over global scale very well, however, it underestimated ENSO teleconnection patterns and associated changes over regional scale (e.g., MENA and South Asia), especially following weak ENSO that could be attributed to model weak response to circulation changes such as Pacific North American (PNA) and North Atlantic Oscillation (NAO). Moreover, our results emphasize that ENSO impacts are relatively stronger over the Inter-Tropical Convergence Zone (ITCZ) compared to extra-tropics and high-latitude regions. The positive phase of ENSO causes weakening in rainfall over the African tropical rain-belt, parts of South and Southeast Asia. Both the reanalysis and HiRAM results reveal that ENSO-induced negative (positive) NAO-like response and associated changes over Southern Europe and North Africa vary significantly following the increased intensity of El Nino (La Nina). We further found that the ENSO magnitude significantly impacts Hadley and Walker circulations. The El Nino phase of ENSO overall strengthens the Hadley Cell, and the reverse is true for the La Nina phase. This ENSO-induced strengthening and weakening of Hadley Cell induce significant impact over South Asian and African convective regions through modification of the ITCZ circulation system.

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.

This study examines the artificial influence of increasing the SST resolution on the storm track over the North Pacific in ERA-Interim. Along with the mesoscale oceanic eddies and fronts resolved during the high-resolution-SST period, the low-level storm track strengthens northward, reaching more than 30% of the maximum values in the low-resolution-SST period after removing the influence of ENSO. The mesoscale structure firstly imprints on the marine atmospheric boundary layer, which then leads to changes in turbulent heat flux and near-surface convergence, forcing a secondary circulation into the free atmosphere, strengthening the vertical eddy heat, momentum and specific humidity fluxes, and contributing to the enhancement of the storm track. Results from a high-resolution atmospheric model further indicate the changes in the storm track due to the mesoscale SST and their relationship.

Severe local storm (SLS) activity is known to occur within specific thermodynamic and kinematic environments. These environments are commonly associated with key synoptic-scale features--including southerly Great Plains low-level jets, drylines, elevated mixed layers, and extratropical cyclones--that link the large-scale climate to SLS environments. This work analyzes spatiotemporal distributions of both the environmental parameters and synoptic-scale features in ERA5 reanalysis and in Community Atmosphere Model version 6 (CAM6) during 1980--2014 over North America. Compared to radiosondes, ERA5 successfully reproduces SLS environments, with strong spatiotemporal correlations and low biases, especially over the Great Plains. Both ERA5 and CAM6 reproduce the climatology of SLS environments over the central United States as well as its strong seasonal and diurnal cycles. ERA5 and CAM6 also reproduce the climatological occurrence of the synoptic-scale features, with the distribution pattern similar to that of SLS environments. Compared to ERA5, CAM6 exhibits a high bias in Convective Available Potential Energy over the eastern United States primarily due to a high bias in surface moisture, and to a lesser extent, storm-relative helicity due to enhanced low-level winds. Composite analysis indicates consistent synoptic anomaly patterns favorable for significant SLS environments over much of the eastern half of the United States in both ERA5 and CAM6, though the pattern differs for the southeastern United States. Overall, results indicate that both ERA5 and CAM6 are capable of reproducing SLS environments as well as the synoptic-scale features and transient events that generate them., 25 pages, 15 figures

The present study evaluates the performance of Conformal-Cubic Atmospheric Model (CCAM) simulations downscaled from six global climate models (GCMs) (i.e., ACCESS1.0, CNRM-CM5, CCSM4, GFDL-CM3, MPI-ESM-LR, and NorESM-M) and Max Plank’s Regional Model (REMO2009(MPI)) obtained from the South-Asia Coordinated Regional Climate Downscaling Experiment (CORDEX) for analyzing the summer monsoon maximum temperature (Tmax) over the fifteen Agro-Climatic Zones (ACZs) in India. The model simulations are compared with the two sets of observed data obtained from the India Meteorology Department (IMD) and Climate Research Unit (CRU) for the period from 1981 to 2005. The results illustrate that the skill of CCAM regional climate models (RCMs) is higher than the REMO in simulating the Tmax over all the regions. The spatial patterns of Tmax in CCAM (CCSM) and CCAM (CNRM) are closer to IMD, while the Tmax distributions in CCAM (CNRM), CCAM (CCSM), and CCAM (BCCR) agree well with the CRU, and correlation coefficient (CC) is more than 0.6; however, large positive biases in all RCMs are depicted over the Himalayan regions. The inter-comparison among all the RCMs suggest that the CCAM (CNRM) and CCAM (CCSM) are rendering as the foremost models in simulating Tmax over different ACZs. Performances of these two models also infer the usefulness of the model products for impact studies over the individual ACZs. However, the existing systematic biases in the RCMs impeded the model performance and it is necessary to remove the model bias prior to some real-time application. In this study, two bias correction methods, i.e., linear scaling (LS) and distribution mapping (DM), have been used to correct RCM output bias. It is found that the model performance using DM correction is better than LS method. The performance validations are evaluated based on the probability density function (PDF), CC, and standard deviation (SD) with 95% confidence level. The model evaluation has also been justified using mean absolute error (MAE) index, Nash-Sutcliffe coefficient (NS) index, percent bias (Pbias), and the Willmott’s index of agreement (d) which confirm the research findings. The results are providing an effective guidance on the usefulness of bias corrected RCMs over a particular ACZs for impact assessment.

When orographic gravity waves (OGWs) break, they dissipate their momentum and energy and thereby influence the thermal and dynamical structure of the atmosphere. This OGW forcing mainly takes place in the middle atmosphere. It is zonally asymmetric and strongly intermittent. So-called OGW hotspot regions have been shown to exert a large impact on the total wave forcing, in particular in the lower stratosphere (LS). Motivated by this we investigate the asymmetrical distribution of the three-dimensional OGW drag (OGWD) for selected hotspot regions in the specified dynamics simulation of the chemistry-climate model CMAM (Canadian Middle Atmosphere Model) for the period 1979–2010. As an evaluation, we first compare zonal mean OGW fluxes and GW drag (GWD) of the model simulation with observations and reanalyses in the northern hemisphere. We find an overestimation of GW momentum fluxes and GWD in the model's LS, presumably attributable to the GW parameterizations which are tuned to correctly represent the dynamics of the southern hemisphere. In the following, we define three hotspot regions which are of particular interest for OGW studies, namely the Himalayas, the Rocky Mountains and East Asia. The GW drags in these hotspot regions emerge as strongly intermittent, a result that can also quantitatively be corroborated with observational studies. Moreover, a peak-detection algorithm is applied to capture the intermittent and zonally asymmetric character of OGWs breaking in the LS and to assess composites for the three hotspot regions. This shows that LS peak OGW events can have opposing effects on the upper stratosphere and mesosphere depending on the hotspot region. Our analysis constitutes a new method for studying the intermittency of OGWs, thereby facilitating a new possibility to assess the effect of particular OGW hotspot regions on middle atmospheric dynamics.

Radiative feedbacks depend on the spatial patterns of sea surface temperature (SST) and thus can change over time as SST patterns evolve—the so-called pattern effect. This study investigates intermodel differences in the magnitude of the pattern effect and how these differences contribute to the spread in effective equilibrium climate sensitivity (ECS) within CMIP5 and CMIP6 models. Effective ECS in CMIP5 estimated from 150-yr-long abrupt4×CO2 simulations is on average 10% higher than that estimated from the early portion (first 50 years) of those simulations, which serves as an analog for historical warming; this difference is reduced to 7% on average in CMIP6. The (negative) net radiative feedback weakens over the course of the abrupt4×CO2 simulations in the vast majority of CMIP5 and CMIP6 models, but this weakening is less dramatic on average in CMIP6. For both ensembles, the total variance in the effective ECS is found to be dominated by the spread in radiative response on fast time scales, rather than the spread in feedback changes. Using Green’s functions derived from two AGCMs shows that the spread in feedbacks on fast time scales may be primarily due to differences in atmospheric model physics, whereas the spread in feedback evolution is primarily governed by differences in SST patterns. Intermodel spread in feedback evolution is well explained by differences in the relative warming in the west Pacific warm-pool regions for the CMIP5 models, but this relation fails to explain differences across the CMIP6 models, suggesting that a stronger sensitivity of extratropical clouds to surface warming may also contribute to feedback changes in CMIP6.

Earth observations were used to evaluate the representation of land surface temperature (LST) and vegetation coverage over Iberia in two state-of-the-art land surface models (LSMs) – the European Centre for Medium-Range Weather Forecasts (ECMWF) Carbon-Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (CHTESSEL) and the Météo-France Interaction between Soil Biosphere and Atmosphere model (ISBA) within the SURface EXternalisée modeling platform (SURFEX-ISBA) for the 2004–2015 period. The results showed that the daily maximum LST simulated by CHTESSEL over Iberia was affected by a large cold bias during summer months when compared against the Satellite Application Facility on Land Surface Analysis (LSA-SAF), reaching magnitudes larger than 10 ∘C over wide portions of central and southwestern Iberia. This error was shown to be tightly linked to a misrepresentation of the vegetation cover. In contrast, SURFEX simulations did not display such a cold bias. We show that this was due to the better representation of vegetation cover in SURFEX, which uses an updated land cover dataset (ECOCLIMAP-II) and an interactive vegetation evolution, representing seasonality. The representation of vegetation over Iberia in CHTESSEL was improved by combining information from the European Space Agency Climate Change Initiative (ESA-CCI) land cover dataset with the Copernicus Global Land Service (CGLS) leaf area index (LAI) and fraction of vegetation coverage (FCOVER). The proposed improvement in vegetation also included a clumping approach that introduces seasonality to the vegetation cover. The results showed significant added value, removing the daily maximum LST summer cold bias completely, without reducing the accuracy of the simulated LST, regardless of season or time of the day. The striking performance differences between SURFEX and CHTESSEL were fundamental to guiding the developments in CHTESSEL highlighting the importance of using different models. This work has important implications: first, it takes advantage of LST, a key variable in surface–atmosphere energy and water exchanges, which is closely related to satellite top-of-atmosphere observations, to improve the model's representation of land surface processes. Second, CHTESSEL is the land surface model employed by ECMWF in the production of their weather forecasts and reanalysis; hence systematic errors in land surface variables and fluxes are then propagated into those products. Indeed, we showed that the summer daily maximum LST cold bias over Iberia in CHTESSEL is present in the widely used ECMWF fifth-generation reanalysis (ERA5). Finally, our results provided hints about the interaction between vegetation land–atmosphere exchanges, highlighting the relevance of the vegetation cover and respective seasonality in representing land surface temperature in both CHTESSEL and SURFEX. As a whole, this work demonstrated the added value of using multiple earth observation products for constraining and improving weather and climate simulations.

The possibility of using the Marine Hindcast and Forecast System (MHFS) developed at Zubov State Oceanographic Institute for short-term forecasting of hydrometeorological and ice characteristics of the Caspian Sea is demonstrated. The presented implementation of the system is based on the regional nonhydrostatic atmospheric model WRF (Weather Research and Forecast Model) and model of marine circulation INMOM (Institute of Numerical Mathematics Ocean Model). The system performance is assessed for the period from January 1 to March 31, 2017 by comparing the forecasts of hydrometeorological characteristics with observational data from hydrometeorological stations. The quality of reproducing sea ice characteristics based on ESIMO objective data is also evaluated.

Regional coupled modeling is one of the frontiers of regional climate modeling, but intercomparison has not been well coordinated. In this study, a community regional climate model, WRF4, with a resolution of 15 km, was coupled with a high-resolution (0.1°) North Pacific Ocean model (LICOM_np). The performance of the regional coupled model, WRF4_LICOM, was compared to that of another regional coupled model, RegCM4_LICOM, which was a coupling of version 4 of the Regional Climate Model (RegCM4) with LICOM_np. The analysis focused on the 2005 western North Pacific summer monsoon rainfall. The results showed that the regional coupled models with either RegCM4 or WRF4 as their atmospheric model component simulated the broad features over the WNP reasonably well. Quantitative intercomparison of the regional coupled simulations exhibited different biases for different climate variables. RegCM4_LICOM exhibited smaller biases in its simulation of the averaged June–July–August SST and rainfall, while WRF4_LICOM better captured the tropical cyclone (TC) intensity, the percentage contributions of rainfall induced by TCs to the total rainfall, and the diurnal cycle of rainfall and stratiform percentages, especially over land areas. The different behaviors in rainfall simulated by the two models were partly ascribed to the behaviors in the simulated western North Pacific subtropical high (WNPSH). The stronger (weaker) WNPSH in WRF4_LICOM (RegCM4_LICOM) was driven by overestimated (underestimated) diabatic heating, which peaked at approximately 450 hPa over the region around the Philippines in association with different condensation-radiation processes. Coupling of WRF4 with LIOCM is a crucial step towards the development of the next generation of regional earth system models at the Chinese Academy of Sciences.

Three tiers of experiments in the Global Monsoons Model Intercomparison Project (GMMIP), one of the endorsed model intercomparison projects of phase 6 of the Coupled Model Intercomparison Project (CMIP6), are implemented by the First Institute of Oceanography Earth System Model version 2 (FIO-ESM v2.0), following the GMMIP protocols. Evaluation of global mean surface air temperature from 1870 to 2014 and climatological precipitation (1979–2014) in tier-1 shows that the atmosphere model of FIO-ESM v2.0 can reproduce the basic observed atmospheric features. In tier-2, the internal variability is captured by the coupled model, with the SST restoring to the model climatology plus the observed anomalies in the tropical Pacific and North Atlantic. Simulation of the Northern Hemisphere summer monsoon circulation is significantly improved by the SST restoration in the North Atlantic. In tier-3, five orographic perturbation experiments are conducted covering the period 1979–2014 by modifying the surface elevation or vertical heating in the prescribed region. In particular, the strength of the South Asian summer monsoon is reduced by removing the topography or thermal forcing above 500 m over the Asian continent. Monthly and daily simulated outputs of FIO-ESM v2.0 are provided through the Earth System Grid Federation (ESGF) node to contribute to a better understanding of the global monsoon system.

Using information from the A-Train satellites, the properties and radiative effects of eastern Pacific Ocean boundary layer clouds are evaluated in the Community Atmosphere Model, version 5 (CAM5), from the summer of 2007 and 2008. The cloud microphysical properties are inferred using measurements from CloudSat and CALIPSO (CC) that are then used to calculate the broadband radiative flux profiles. Accounting appropriately for sampling differences between the measurements and the simulation, evidence of the “too few, too bright” low cloud bias is found in CAM5. Single-layer low clouds have a frequency of occurrence of 42% from CC, as compared with just 29% in CAM5, and the averaged cloud radiative kernel (CRK) for the model shows stronger cooling. For stratocumulus in particular, the cooling in the model CRK is larger by a factor of 2 relative to the observations, implying an overly sensitive tropical low cloud feedback. Differences in the day/night occurrence of stratocumulus help to explain some of the difference in the CRK. The cloud-type microphysics for liquid clouds is represented reasonably well by the model, with a tendency for smaller water paths and smaller effective radii. Overall, the occurrence and CRK have partially compensating errors such that the net cooling at the top of the atmosphere for eastern Pacific low clouds is −43 W m−2 in CAM5, as compared with −32 W m−2 from CC. The cooling effect in the model is accomplished by fewer low clouds with a narrower range of properties, as compared with more clouds with a broader range of properties in the observation-based dataset.

El Nino-Southern Oscillation (ENSO) is the strongest interannual signal that is produced by basinscale processes in the tropical Pacific, with significant effects on weather and climate worldwide. In the past, extensive and intensive international efforts have been devoted to coupled model developments for ENSO studies. A hierarchy of coupled ocean-atmosphere models has been formulated; in terms of their complexity, they can be categorized into intermediate coupled models (ICMs), hybrid coupled models (HCMs), and fully coupled general circulation models (CGCMs). ENSO modeling has made signifiscant progress over the past decades, reaching a stage where coupled models can now be used to successfully predict ENSO events 6 months to one year in advance. Meanwhile, ENSO exhibits great diversity and complexity as observed in nature, which still cannot be adequately captured by current state-of-the-art coupled models, presenting a challenge to ENSO modeling. We primarily reviewed the long-term efforts in ENSO modeling continually and steadily made at different institutions in China; some selected representative examples are presented here to review the current status of ENSO model developments and applications, which have been actively pursued with noticeable progress being made recently. As ENSO simulations are very sensitive to model formulations and process representations etc., dedicated efforts have been devoted to ENSO model developments and improvements. Now, different ocean-atmosphere coupled models have been available in China, which exhibit good model performances and have already had a variety of applications to climate modeling, including the Coupled Model Intercomparison Project Phase 6 (CMIP6). Nevertheless, large biases and uncertainties still exist in ENSO simulations and predictions, and there are clear rooms for their improvements, which are still an active area of researches and applications. Here, model performances of ENSO simulations are assessed in terms of advantages and disadvantages with these differently formulated coupled models, pinpointing to the areas where they need to be further improved for ENSO studies. These analyses provide valuable guidance for future improvements in ENSO simulations and predictions.

The response of the North Pacific storm track to the mesoscale sea surface temperature (SST) in winter is investigated via a global high-resolution atmospheric model. A simulation forced by eddy-resolving SST is compared with a simulation in which the mesoscale SST is filtered out. The results show that removing the mesoscale SST could greatly influence the storm track in the free atmosphere, with a significant decrease of approximately 20% in the local region and a southward shift downstream over the eastern North Pacific. Compared with those in previous studies, the responses of the storm track seem to be independent from models. The underlying mechanism is that changes in the boundary layer induced by mesoscale SST lead to convergence at the surface through pressure adjustment, forcing a secondary circulation along Kuroshio and Oyashio confluence region (KOCR). Then the winter mean vertical eddy fluxes are greatly suppressed over KOCR after removing the mesoscale SST, transporting less heat and moisture into the free atmosphere. Furthermore, the response of baroclinicity and baroclinic energy conversion was investigated, which bears much resemblance with the changes of storm track, indicating the important role on the response of storm track to mesoscale SST.

The Madden Julian oscillation (MJO) has a systematic influence on the state of the extratropical atmosphere. The physical mechanism involves teleconnections from tropical deep convective heat sources through wave propagation. MJO heating resembles a propagating dipole that cycles through phases on a timescale similar to the response timescale. This complicates the attribution of the response to a well identified phase in the tropical heat source. In this paper the Dynamical Research Empirical Atmospheric Model (DREAM) is used to examine the extratropical response to a moving cyclic MJO-like tropical heat source based on outgoing long-wave radiation data. A set of ensemble forecast experiments and consistent stationary wave solutions are analysed, together with longer equilibrium integrations, to investigate how the extratropical response relates to the phase of the MJO heating. We find that the response is modified by the propagation of the source in a way that depends on the initial phase. It also displays both stationary and transient nonlinearity which can account for an asymmetric response to opposite-phase dipole heat sources. The solution for Indian Ocean heating/West Pacific cooling is more zonally oriented and weaker than the solution for the opposite phase. If the MJO heat source is specified as a continuous cycle, it modifies the time-mean extratropical circulation and generates remote teleconnections. The timing and starting phase of MJO events can greatly modify the relationship between MJO phase and extratropical response, especially when an unforced “resting” period is inserted between simulated MJO cycles.

As a contribution to understanding the genesis of tropical cyclones (TCs), we compared TC genesis processes simulated by the Seoul National University Atmosphere Model version 0 with a Unified Convection Scheme (SAM0) and the Community Atmosphere Model version 5 (CAM5) with those from the ERA-Interim reanalysis (ERA-Interim, hereafter ERAI) and best track observations. In contrast to previous studies that estimated the TC genesis potential using the Eulerian mean environmental conditions, we calculated the probability of a pre-existing weak cyclonic embryo vortex (EV) developing into a TC by analyzing changes in the environmental conditions along the EV trajectories. Our analysis indicates that the spatial distribution and annual cycles of TCs obtained from the SAM0 and ERAI are similar to those obtained from the best track observation data. With the exception of the mesoscale convective organization and associated variables, most environmental variables along the trajectories of DEVs (EVs developing into TCs) showed monotonic variations. When EVs were born, environmental conditions of DEVs were significantly different from those of nondeveloping EVs, allowing for the prediction of TC genesis. In general, TC genesis probability increased as the environment became more cyclonic, moist, unstable, and with a weaker wind shear. Rapidly strengthening EVs were more likely to develop into TCs. SAM0 and ERAI have the same combination of environmental variables with the best prediction skill for TC genesis—absolute vorticity at 850 hPa, column saturation deficit, sea surface temperature, vertical shear of horizontal winds between 200 and 850 hPa, and latitude—with similar sensitivities to individual environmental variables, indicating that SAM0 well simulates the observed TC genesis processes.

We analyze the climate change signal in the Mediterranean Sea using the regionally coupled model REMO–OASIS–MPIOM (ROM; abbreviated from the regional atmosphere model, the OASIS3 coupler and the Max Planck Institute Ocean Model). The ROM oceanic component is global with regionally high horizontal resolution in the Mediterranean Sea so that the water exchanges with the adjacent North Atlantic and Black Sea are explicitly simulated. Simulations forced by ERA-Interim show an accurate representation of the present Mediterranean climate. Our analysis of the RCP8.5 (representative concentration pathway) scenario using the Max Planck Institute Earth System Model shows that the Mediterranean waters will be warmer and saltier throughout most of the basin by the end of this century. In the upper ocean layer, temperature is projected to have a mean increase of 2.7 ∘C, while the mean salinity will increase by 0.2 psu, presenting a decreasing trend in the western Mediterranean in contrast to the rest of the basin. The warming initially takes place at the surface and propagates gradually to deeper layers. Hydrographic changes have an impact on intermediate water characteristics, potentially affecting the Mediterranean thermohaline circulation in the future.

To understand the effect of the time step on the physics–dynamics interaction in a model, we used an idealized tropical cyclone test to evaluate the sensitivities to the physics time step in the Community Atmosphere Model Version 5 (CAM5). The investigated time steps were 450, 900 and 1800 s at a resolution of 1°, and 225, 450, 900 and 1800 s at a resolution of 0.25° in the corresponding ensemble simulations. We found that the intensity and precipitation of the simulated tropical cyclone and the physics parameterizations are fairly sensitive to the time step. These sensitivities are affected by the dynamical core and the physics–dynamics coupling strategy and vary with the horizontal resolution. In low-resolution runs, the intensity of the simulated tropical cyclone varies little with physics time step in the finite volume (FV) dynamical core, but it tends to weaken with decreasing time steps in the spectral element (SE) dynamical core. The horizontal circulation of the tropical cyclone in both the FV and SE simulations increases as the length of the time step decreases in high-resolution runs, where large-scale condensation dominates. The sensitivities in the physical parameterizations to time step play an important role in regulating the impact of time step on the physics–dynamics interaction, especially in high-resolution simulations. Compared with the sequential coupling approach (ftype1) with a sudden adjustment at each physics time step in the SE core, the dribbling coupling strategy (ftype0) that adjusts the state more gradually weakens the effect of the physical parameterizations.