Wet Bias Research Articles

Challenge of modelling GLORIA observations of upper troposphere–lowermost stratosphere trace gas and cloud distributions at high latitudes: a case study with state-of-the-art models

Abstract. Water vapour and ozone are important for the thermal and radiative balance of the upper troposphere (UT) and lowermost stratosphere (LMS). Both species are modulated by transport processes. Chemical and microphysical processes affect them differently. Thus, representing the different processes and their interactions is a challenging task for dynamical cores, chemical modules and microphysical parameterisations of state-of-the-art atmospheric model components. To test and improve the models, high-resolution measurements of the UT–LMS are required. Here, we use measurements taken in a flight of the GLORIA (Gimballed Limb Observer for Radiance Imaging of the Atmosphere) instrument on HALO (High Altitude and LOng Range Research Aircraft). The German research aircraft HALO performed a research flight on 26 February 2016 that covered deeply subsided air masses of the aged 2015/16 Arctic vortex, high-latitude LMS air masses, a highly textured region affected by troposphere-to-stratosphere exchange and high-altitude cirrus clouds. Therefore, it provides a challenging multifaceted case study for comparing GLORIA observations with state-of-the-art atmospheric model simulations in a complex UT–LMS region at a late stage of the Arctic winter 2015/16. Using GLORIA observations in this manifold scenario, we test the ability of the numerical weather prediction (NWP) model ICON (ICOsahedral Nonhydrostatic) with the extension ART (Aerosols and Reactive Trace gases) and the chemistry–climate model (CCM) EMAC (ECHAM5/MESSy Atmospheric Chemistry – fifth-generation European Centre Hamburg general circulation model/Modular Earth Submodel System) to model the UT–LMS composition of water vapour (H2O), ozone (O3), nitric acid (HNO3) and clouds. Within the scales resolved by the respective model, we find good overall agreement of both models with GLORIA. The applied high-resolution ICON-ART set-up involving an R2B7 nest (local grid refinement with a horizontal resolution of about 20 km), covering the HALO flight region, reproduces mesoscale dynamical structures well. Narrow moist filaments in the LMS observed by GLORIA at tropopause gradients in the context of a Rossby wave breaking event and in the vicinity of an occluded Icelandic low are clearly reproduced by the model. Using ICON-ART, we show that a larger filament in the west was transported horizontally into the Arctic LMS in connection with a jet stream split associated with poleward breaking of a cyclonically sheared Rossby wave. Further weaker filaments are associated with an older tropopause fold in the east. Given the lower resolution (T106) of the nudged simulation of the EMAC model, we find that this model also reproduces these features well. Overall, trace gas mixing ratios simulated by both models are in a realistic range, and major cloud systems observed by GLORIA are mostly reproduced. However, we find both models to be affected by a well-known systematic moist bias in the LMS. Further biases are diagnosed in the ICON-ART O3, EMAC H2O and EMAC HNO3 distributions. Finally, we use sensitivity simulations to investigate (i) short-term cirrus cloud impacts on the H2O distribution (ICON-ART), (ii) the overall impact of polar winter chemistry and microphysical processing on O3 and HNO3 (ICON-ART and EMAC), (iii) the impact of the model resolution on simulated parameters (EMAC), and (iv) consequences of scavenging processes by cloud particles (EMAC). We find that changing the horizontal model resolution results in notable systematic changes for all species in the LMS, while scavenging processes play a role only in the case of HNO3. We discuss the model biases and deficits found in this case study that potentially affect forecasts and projections (adversely) and provide suggestions for further model improvements.

Evaluation of Six Satellite-Based Soil Moisture Products Based on in situ Measurements in Hunan Province, Central China

Long-term near-surface soil moisture (SM) data can be obtained on a regional scale through microwave remote sensing. Therefore, to quantitatively analyze the accuracy of multisource remote sensing–based observation products, improve the retrieval algorithm, and effectively use in terminal environments, a standardized comprehensive evaluation is imperative. The SM data obtained by the China Meteorological Administration and Ministry of Water Resources were used as reference data to verify the performance of six passive microwave remote sensing–based SM products from the SMOS, SMAP, GCOM-W, FY-3B, and FY-3C satellites in Hunan province, China. These data were also used to analyze the effects of topographical, land cover, and meteorological factors on SM retrieval accuracy. Results show that SMAP shows the best overall performance in Hunan province; furthermore, it exhibits stable accuracy and is not easily affected by environmental factors. The FY series of satellite products shows the worst performance, and some grid remote sensing data are negatively correlated with the ground measurement data. AMSR2 possesses the largest amount of data and the largest deviation, and only this product exhibits significant differences with the fluctuation trend of the measured SM and precipitation. Passive microwave detection technology presents the best performance in the central part of Hunan province and the largest bias in the Dongting Lake area. SMOS-L3 and SMOS-IC, two products of the same satellite, show the lowest bias but present differences in the SM fluctuation range, orbital accuracy, as well as dry or wet bias. Furthermore, FY-3B and FY-3C, two satellites belonging to the same series, exhibit excellent consistency in performance. The evaluation results and accuracy variation between products as well as other factors identified in the study provide a baseline reference for improving the retrieval algorithm. This study provides a quantitative basis for developing improved applications of passive microwave SM products.

Evaluation of a Reanalysis‐Driven Configuration of WRF4 Over the Western United States From 1980 to 2020

AbstractDynamical downscaling remains a powerful tool for studying regional climate processes, and the genesis of high‐resolution historical and future climate data. This technique is particularly important over areas of complex terrain, such as the western United States (WUS), where global models are especially limited in representing regional climate. After identifying a suite of WRF options that best simulate snow and precipitation for an average water year (2010) over the WUS, we evaluate the performance of the dynamically downscaled European Centre for Medium‐range Weather Forecasting's fifth Reanalysis (ERA5) from 1980 to 2020 on 45‐km, 9‐km, and two 3‐km grids. We find that by decreasing the horizontal grid spacing within WRF, improvements to Sierra Nevada and Northern Rocky Mountain snow, Santa Ana and Diablo winds, and coastal meteorology occur. For landfalling atmospheric rivers (ARs), the downscaled reanalysis simulates greater upstream integrated vapor transport (IVT) than ERA5. However, WRF skillfully simulates the positioning of the IVT and the timing and magnitude of AR precipitation. This potential IVT bias, in conjunction with increasing resolution, leads to a wet precipitation bias across the Sierra Nevada in the 3‐km experiment. This conclusion is supported by streamflow analysis, although we note that the bias in the 3‐km experiment can also be explained by in situ undercatch issues. Meanwhile, the 9‐km experiment is more biased than the 3‐km experiment across the Northern Rocky Mountains compared to in situ measured SWE and precipitation, indicating a geographic sensitivity to biases.

Comprehensive assessment of RegCM4 towards interannual variability of Indian Summer Monsoon using multi-year simulations

In this study, the interannual variability (IAV) of Indian Summer Monsoon (ISM) is investigated using multi-year (1982‒2016) seasonal scale simulations (May‒September) of the regional climate model RegCM4. Model-simulated fields such as surface temperature, wind and rainfall are validated initially to testify the climatological behaviour of ISM. Subsequently, different aspects of IAV associated with ISM are discussed primarily focusing on model simulated rainfall and are verified against high-resolution rainfall analysis from India Meteorological Department (IMD). Analysis indicated that RegCM4 shows reasonable accuracy in simulating major large-scale features, however, has cold bias over entire India and wet (dry) bias over northwest and peninsular (central) India. Easterly (westerly) bias is noticed in the model simulated low (upper) level wind that affects regional Hadley circulation. The cold bias is found to be associated with the feedback cycle of land–atmosphere interaction. Surface evaporative cooling likely affects the static instability in the atmospheric column, thereby limiting the convection and thus reducing rainfall. While categorizing, it is noticed that the efficacy of the model is found to be better in simulating normal monsoon as compared to contrasting monsoon (deficit and excess) year, thereby reducing the simulation skill for the entire period. EOF analysis revealed that first two leading modes of IMD rainfall are linked with large-scale variabilities, viz. El-Nino Southern Oscillation and Indian Ocean Dipole, respectively, but RegCM4 could not well reproduce these relationships. The spectral analysis showed 2–7 year periodicity in the model. However, the associated spectral peaks are close to the red noise spectrum due to their weak power suggesting limited model skill to capture large-scale variability. Overall, this study advocates that the RegCM4 could capture the climatological features of ISM fairly well, but needs further improvement in representing the IAV more accurately.

Improvements in Diurnal Cycle and Its Impact on Seasonal Mean by Incorporating COARE Flux Algorithm in CFS

The variability of predicted variables at daily to seasonal scales in coupled models is primarily governed by surface boundary conditions between the ocean and atmosphere, namely, sea surface temperature (SST), turbulent heat, and momentum fluxes. Although efforts have been made to achieve good accuracy in surface fluxes and SST in observation and reanalysis products, less attention has been paid toward achieving improved accuracy in coupled model simulations. Improper diurnal phase and amplitude in intra-daily SST and precipitation are well-known problems in most global coupled general circulation models, including the Climate Forecast System v2 (CFSv2) model. The present study attempts to improve the representation of ocean-atmosphere surface boundary conditions in CFSv2, primarily used for India's operational forecasts at different temporal/spatial scales. In this direction, the diurnal warm layer and cool skin temperature correction scheme are implemented along with the surface flux parameterization scheme following Coupled Ocean-Atmosphere Response Experiment (COARE) v 3.0. The coupled model re-forecasts with a revised flux scheme showed better characteristics in various ocean-atmosphere parameters and processes at diurnal and seasonal time scales. At the diurnal scale, the phase and amplitude of intra-daily SST and mixed layer depth variabilities are improved over most tropical oceans. Improved diurnal SSTs helped in enhancing the diurnal range of precipitation by triggering stronger intra-daily convection. The corrected diurnal ocean-atmospheric boundary state translated into a reduction in seasonal mean dry bias over Indian landmass and the wet bias over tropical oceans. Better simulation of non-linearity associated with El Niño–Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), ENSO-Indian Summer Monsoon Rainfall (ISMR), and IOD-ISMR relation is among the most critical improvements achieved by revising the turbulent flux parameterization. The revised flux scheme showed enhanced prediction skills for tropical SST indices and ISMR.

Weakened impact of the Atlantic Niño on the future equatorial Atlantic and Guinea Coast rainfall

Abstract. The Guinea Coast is the southern part of the West African continent. Its summer rainfall variability mostly occurs on interannual timescales and is highly influenced by the sea surface temperature (SST) variability in the eastern equatorial Atlantic, which is the centre of action of the Atlantic Niño mode. Using both historical and scenario (SSP5–8.5) simulations from 31 general circulation models (GCMs) participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6), we first show that these models present a wet bias during boreal summer. This bias is associated with overly high mean boreal summer SSTs in the eastern equatorial and south Atlantic regions. Next, we analyse the near-term, mid-term and long-term changes of the Atlantic Niño relative to the present-day situation, in a climate with a high anthropogenic emission of greenhouse gases. We find a gradual decrease in the equatorial Atlantic SST anomalies associated with the Atlantic Niño in the future. This result reflects a possible reduction of the Atlantic Niño variability in the future due to a weakening of the Bjerknes feedback over the equatorial Atlantic. In a warmer climate, an anomalous higher sea level pressure in the western equatorial Atlantic relative to the eastern equatorial Atlantic weakens the climatological trade winds over the equatorial Atlantic. As a result, the eastern equatorial Atlantic thermocline is deeper and responds less to the Atlantic Niño events. Among the models that simulate a realistic rainfall pattern associated with the Atlantic Niño in the present-day climate, there are 12 GCMs which project a long-term decrease in the Guinea Coast rainfall response related to the Atlantic Niño. In these models, the zonal 850 hPa wind response to the Atlantic Niño over the equatorial Atlantic is strongly attenuated in the future climate. We also find that 12 other GCMs show no robust change in the patterns associated with the Atlantic Niño. There is a higher confidence in the mid-term and long-term reduction of the rainfall associated with the Atlantic Niño over the Atlantic Ocean than over the Guinea Coast. We also found a projected decrease in the convection associated with the Atlantic Niño in the majority of the models.

Understanding the Present-Day Spatiotemporal Variability of Precipitable Water Vapor over Ethiopia: A Comparative Study between ERA5 and GPS

Atmospheric water vapor plays a crucial role in atmospheric, climate change, meteorological, and hydrological processes. In a country like Ethiopia, with its complex topography and synoptic-scale spatiotemporal circulation patterns, the analysis of the spatiotemporal variability of precipitable water vapor (PWV) is very challenging, and is hampered by the lack of long observational datasets. In this study, we process the PWV over eight Ethiopian global positioning system (GPS) sites and one close to the Ethiopian eastern border, for the available common period 2013–2020, and compare with the PWV retrieved from the state-of-the-art ERA5 reanalysis. Both PWV datasets agree very well at our sample, with correlation coefficients between 0.96 and 0.99, GPS-PWV show a moderate wet bias compared to ERA5-PWV for the majority of the sites, and an overall root mean square error of 3.4 mm. Seasonal and diurnal cycles are also well captured by these datasets. The seasonal variations of PWV and precipitation at the sites agree very well. Maximum diurnal PWV amplitudes are observed for stations near water bodies or dense vegetation, such as Arbaminch (ARMI) and Bahir Dar (BDMT). At those stations, the PWV behavior at heavy rainfall events has been investigated and an average 25% increase (resp. decrease) from 12 h before (resp. 12 h after) the start of the rainfall event, when the PWV peaks, has been observed.

TROPOspheric Monitoring Instrument observations of total column water vapour: Algorithm and validation

In this paper, we present the total column water vapour (TCWV) retrieval for the TROPOspheric Monitoring Instrument (TROPOMI) observations in the visible blue spectral band. The TROPOMI TCWV algorithm is being optimized and validated in the framework of the Sentinel 5 Precursor Product Algorithm Laboratory (S5P-PAL) project from the European Space Agency (ESA). The retrieval was first developed to retrieve TCWV from the Global Ozone Monitoring Experiment 2 (GOME-2). We have optimized the settings of the retrieval to adapt it for TROPOMI observations. The TROPOMI TCWV algorithm follows the typical two step approach, using spectral fit retrieval of slant columns, and conversion of the slant columns to vertical columns using air mass factors (AMFs). An iterative optimization algorithm is developed to dynamically find the optimal a priori water vapour profile for the AMF calculation. Further optimizations on the spectral retrieval, air mass factor calculations as well as a new surface albedo retrieval approach are implemented.The TCWV retrieval algorithm is applied to TROPOMI observations from May 2018 to May 2021. The results are validated by comparing them to ERA5 reanalysis data, GOME-2, MODerate resolution Imaging Spectroradiometer (MODIS) and Special Sensor Microwave Imager Sounder (SSMIS) satellite observations. TCWV derived from TROPOMI observations agree well with the other data sets with Pearson correlation coefficient (R) ranging from 0.96 to 0.99. The mean bias between TROPOMI and ERA5 data is −1.24 kg m−2 for measurements over land and 0.73 kg m−2 for measurements over water. The comparison to MODIS observations show similar results with small dry bias of 1.51,kg m−2 for measurements over land and a small wet bias of 1.25 kg m−2 for measurements over water. Slightly larger dry bias of 1.98 kg m−2 for measurements over land and 1.74 kg m−2 for measurements over water are found when compared to GOME-2 obserations. Compared to SSMIS data over water, TROPOMI observations are bias low by 3.25 kg m−2. The small discrepancies found between TROPOMI and reference data sets are related to the differences in measurement technique, measurement time, surface albedo issue, as well as cloud and aerosol contamination. This study demonstrates that the algorithm can provide stable and consistent results on a global scale and can be applied to generate operational TCWV products from TROPOMI and the forthcoming Copernicus missions Sentinel-4 and Sentinel-5. We have also demonstrated the capability of retrieving fine scale water vapour structures in a case study over the Amazon. This indicates that the TROPOMI data set is also useful for local and regional climate studies.

Assessment of heavy rainfall forecasts over the southern Vietnam by using WRF-ARW with different physical parameterization schemes

This research aims to investigate heavy rainfall forecasting over southern Vietnam (SVN) by using the Weather Research and Forecasting with the Advanced Research dynamical solver (WRF-ARW) model. The selection of 34 heavy rainfall events from 2012 to 2016 over SVN during the active phase of the Southeastern Asian summer monsoon was studied. Sixty different WRF-ARW configurations are determined from three cumulus schemes (Kain–Fritsch with three different types of trigger mechanisms, Grell–Devenyi and Betts– Miller–Janjic), three planetary boundary schemes (Yonsei University, Mellor–Yamada–Janjic and Bougeault-Lacarrère) and four cloud microphysics schemes (Lin, Thompson and two WRF microphysics schemes with five and six moments). Typical skill scores are calculated based on the surface observation data and spatial verifications are carried out using the fraction skill score (FSS) and grid rainfall observations from the satellite-based GSMaP product. It is clear that the most sensitive to heavy rainfall forecasts were cumulus scheme selections, especially at thresholds below 50 mm/24 h. The configurations using the Kain–Fritsch scheme have wet bias issues. However, overall, in terms of a high probability of detection (POD) and high threat scores (CSI), the most skillful configuration was the use of the Kain–Fritsch scheme with the third trigger mechanism in combination with local diffusion planetary boundary schemes.

Simulation of Arctic Thin Ice Clouds with Canadian Regional Climate Model Version 6: Verification against CloudSat-CALIPSO

Polar clouds are, as a consequence of the paucity of in situ observations, poorly understood compared to their lower latitude analogs, yet highly climate-sensitive through thermal radiation emission. The prevalence of Thin Ice Clouds (TIC) dominates in cold Polar Regions and the Upper Troposphere Lower Stratosphere (UTLS) altitudes. They can be grouped into 2 broad categories. The first thin ice cloud type (TIC1) is made up of high concentrations of small, non-precipitating ice crystals. The second type (TIC2) is composed of relatively small concentrations of larger, precipitating ice crystals. In this study, we investigate the ability of a developmental version of the Canadian Regional Climate Model (CRCM6) in simulating cold polar-night clouds over the Arctic Ocean, a remote region that is critical to atmospheric circulation reaching out to the mid-latitudes. The results show that, relative to CloudSat-CALIPSO vertical profile products, CRCM6 simulates high-latitude and low spatial frequency variations of Ice Water Content (IWC), effective radius (re) and cooling rates reasonably well with only small to moderate wet and dry biases. The model can also simulate cloud type, location, and temporal occurrence effectively. As well, it successfully simulated higher altitude TIC1 clouds whose small size evaded CloudSat detection while being visible to CALIPSO.

Disentangling the effect of regional SST bias on the double-ITCZ problem

This study investigates the causes of the double intertropical convergence zone (ITCZ) bias, characterized by too northward northern Pacific ITCZ, too dry equatorial Pacific, and too zonally elongated southern Pacific rainband. While the biases within one fully coupled model GFDL CM2.1 are examined, the large-scale bias patterns are broadly common to CMIP5/6 models. We disentangle the individual contribution of regional sea surface temperature (SST) biases to the double-ITCZ bias pattern using a series of slab ocean model experiments. A previously suggested Southern Ocean warm bias effect in displacing the zonal-mean ITCZ southward is manifested in the northern Pacific ITCZ while having little contribution to the zonally elongated wet bias south of the equatorial Pacific. The excessive southern Pacific precipitation is instead induced by the warm bias along the west coast of South America. The Southern Ocean bias effect on the zonal-mean ITCZ position is diminished by the neighboring midlatitude bias of opposite sign in GFDL CM2.1. As a result, the northern extratropical cold bias turns out to be most responsible for a southward-displaced zonal-mean ITCZ. However, this southward ITCZ displacement results from the northern Pacific branch, so ironically fixing the extratropical biases only deteriorates the northern Pacific precipitation bias. Thus, we emphasize that the zonal-mean diagnostics poorly represent the spatial pattern of the tropical Pacific response. Examination of longitude-latitude structure indicates that the overall tropical precipitation bias is mostly locally driven from the tropical SST bias. While our model experiments are idealized with no ocean dynamics, the results shed light on where preferential foci should be applied in model development to improve particular features of tropical precipitation bias.

Object‐Based Evaluation of Precipitation Systems in Convection‐Permitting Regional Climate Simulation Over Eastern China

AbstractBased on an object‐based tracking (OT) algorithm, the precipitation systems in the convection‐permitting (CP, ∼4 km) regional climate model (RCM) are evaluated from the Lagrangian viewpoints. The characteristics of precipitation systems over eastern China during 1998–2007 derived from the CP RCM are compared with the satellite‐derived precipitation data set CMORPH. It is found that, with the cumulus parameterization switched off and reasonable representation of complex terrain, the precipitation systems in the CP RCM can propagate in the same direction as observation, but CP RCM tends to under‐/overpredict the precipitation systems with slow/fast propagation speed. Nevertheless, CP RCM can reasonably capture the average duration and eccentricity of all precipitation systems. The major deficiency in CP RCM is that the simulated precipitation systems are significantly weaker with larger coverage area. Over‐/underrepresentation of precipitation systems driven by large‐scale circulations/intense thunderstorms in CP RCM leads to wet and dry biases in the total precipitation of the longer‐duration (≥48 hr) and shorter‐duration (<48 hr) systems, respectively. It is suggested that assessment of the lifecycles of propagating precipitation systems in the Lagrangian framework is complementary to the traditional point‐to‐point Eulerian validation method.

Assessment of ERA5-Land Volumetric Soil Water Layer Product Using In Situ and SMAP Soil Moisture Observations

In preparation for the NISAR mission soil moisture algorithm, this study performs the validation of the European Centre for Medium-Range Weather Forecast (ECMWF) ERA5-Land volumetric soil water (soil moisture) layer product with in-situ measurements from the Soil Moisture Active Passive (SMAP) core validation sites (CVS) for 2015 – 2021. The ERA5-Land soil water layer was also compared against the SMAP-enhanced radiometer soil moisture product (gridded at 9 km) at a global extent. For comparison between the ERA5-Land and the SMAP soil moisture products, a matching temporal dataset was generated from ERA5-Land based on the acquisition time of SMAP for each 9 km grid. In comparison with the CVS in-situ measurement, the ERA5-Land data exhibits an overall ubRMSE of about ~0.05 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> but has high wet bias over most of the sites except for sites in Australia, Denmark, and Argentina. The global comparison of the ERA5-Land soil moisture with the SMAP 9 km gridded product shows an overall wet bias with a high correlation in the tropical and temperate regions. The lowest bias was observed over the desert region but has poor correlation as it doesn’t have enough soil moisture variability. Poor correlation with high bias and high RMSD observed over dense vegetated and tundra regions is possibly due to the inferior performance of the SMAP soil moisture product, which has not been validated for these areas.

Skill of the extended range prediction (NERP) for Indian summer monsoon rainfall with NCMRWF global coupled modelling system

AbstractAn extended range prediction system (NERP) based on the Unified global coupled modelling system has been implemented at National Centre for Medium Range Weather Forecasting (NCMRWF). Predictions of weekly anomalies for rainfall, winds and surface temperature are issued once a week for the subsequent 4 weeks. This study is an assessment of the skill of the NERP system over India during the summer monsoon period of June to September (JJAS). The week‐1 and week‐2 rainfall anomaly forecasts exhibit good prediction skill over the Indian region. The evaluations made on meteorologically homogeneous regions of India show moderate to good skill even in week‐3 and week‐4 forecasts for some regions. The variability of the monsoon seasonal cycle in the model is well represented along with the magnitude of summer monsoon rainfall. The model brings out the mean monsoon rainfall features – the peaks during JJAS occurring over the Western Ghats, the Arakan coast and the Bay of Bengal. The onset of the monsoon and the interannual variability of the northward propagation of the rainfall anomalies over the Indian longitudes match reasonably well with observations. The model has some wet bias over the equatorial Indian Ocean and mild dry bias over the Indian land region that develop during the 4‐week period. The anomaly correlations of rainfall are significant over most of the subdivisions of India till week‐3 and week‐4. Probabilistic scores of reliability Receiver Operating Characteristic (ROC) and fair continuous ranked probability score (FCRPS) show that the model exhibits good skill over most regions till week‐2 and moderate skill in weeks 3–4 over other regions. The estimates of forecast reliability are low, a likely consequence of the limited ensemble size. A case‐study of year 2018 showed that the JJAS rainfall regime shifts from wet to dry and vice versa are captured well.

Subseasonal prediction performance for South American land–atmosphere coupling in extended austral summer

AbstractLand–atmosphere feedbacks, through water and energy exchanges, provide subseasonal‐to‐seasonal predictability of the hydrological cycle. We analyse subseasonal land–atmosphere coupling over South America (SA) during extended austral summer for the soil moisture‐to‐precipitation and soil moisture‐to‐air temperature feedback pathways. We evaluate subseasonal hindcasts from global forecasting systems from the UK Met Office, the National Centers for Environmental Prediction (NCEP), the European Centre for Medium Range Weather Forecasts and the Center for Weather Forecast and Climate Studies (CPTEC), for the common period of 1999–2010, against two reanalyses. Biases in land–atmosphere states are established in the first week of hindcasts and increase with lead time. By Week 5, all the models only demonstrate good performance over northern, northeastern and southeastern SA for soil moisture and evapotranspiration and over tropical and subtropical SA for temperature. The hindcasts show stronger coupling at longer lead–lag between variables than reanalyses. Our results highlight possible deficiencies in feedbacks between soil moisture and precipitation in CPTEC and NCEP forecasts over the Amazon due to initial dry soil moisture biases, and in feedbacks between soil moisture and temperature for all four investigated models over southeastern SA due to erroneous representations of evapotranspiration.

Onshore convection associated with the easterly wave over the South China Sea: A case study

A heavy rainfall event associated with a low-level shear line, a mid-level easterly wave, and a marine boundary layer jet occurred from 17 to 18 July 2017 along the coastal area of South China. The shear line was found associated with a low-level disturbance on the northwestern edge of the Luzon Island, while the easterly wave exerted significant influences on its maintenance and development over the South China Sea (SCS). Using the Global Forecast System (GFS) analysis and numerical simulations, the mechanism for the disturbance development is investigated. Along with its development, the disturbance extended upwards to the mid-level easterly wave and established an eastward-tilting structure under the effect of the vertical wind shear. When it approached the coast of South China, the height of the disturbance was lowered down due to the intensified divergence above it, and a slanting updraft was formed. A quasi-closed circulation within the easterly wave helped to protect the moist air inside and was favorable for sustained convection. The westward component of the divergent flow, which was induced by the downdraft below the easterly wave, converged in the disturbance that proved to be the primary mechanism for the disturbance development, while the cold core formed in the downdraft intensified the local baroclinicity and contributed to convection development. Although the simulation reproduced the disturbance evolution, it failed to reproduce the vertically tilting structure of the disturbance. In addition, the convection intensity in the simulation was overestimated due to the wet biases in the low troposphere. Sensitivity experiments showed that the disturbance moved faster and more westwards, when it was weakened through terrain modification, which led to stronger boundary layer jet and heavier precipitation.

Evaluation of Asian summer precipitation in different configurations of a high-resolution general circulation model in a range of decision-relevant spatial scales

Abstract. High-resolution general circulation models (GCMs) can provide new insights into the simulated distribution of global precipitation. We evaluate how summer precipitation is represented over Asia in global simulations with a grid length of 14 km. Three simulations were performed: one with a convection parametrization, one with convection represented explicitly by the model's dynamics, and a hybrid simulation with only shallow and mid-level convection parametrized. We evaluate the mean simulated precipitation and the diurnal cycle of the amount, frequency, and intensity of the precipitation against satellite observations of precipitation from the Climate Prediction Center morphing method (CMORPH). We also compare the high-resolution simulations with coarser simulations that use parametrized convection. The simulated and observed precipitation is averaged over spatial scales defined by the hydrological catchment basins; these provide a natural spatial scale for performing decision-relevant analysis that is tied to the underlying regional physical geography. By selecting basins of different sizes, we evaluate the simulations as a function of the spatial scale. A new BAsin-Scale Model Assessment ToolkIt (BASMATI) is described, which facilitates this analysis. We find that there are strong wet biases (locally up to 72 mm d−1 at small spatial scales) in the mean precipitation over mountainous regions such as the Himalayas. The explicit convection simulation worsens existing wet and dry biases compared to the parametrized convection simulation. When the analysis is performed at different basin scales, the precipitation bias decreases as the spatial scales increase for all the simulations; the lowest-resolution simulation has the smallest root mean squared error compared to CMORPH. In the simulations, a positive mean precipitation bias over China is primarily found to be due to too frequent precipitation for the parametrized convection simulation and too intense precipitation for the explicit convection simulation. The simulated diurnal cycle of precipitation is strongly affected by the representation of convection: parametrized convection produces a peak in precipitation too close to midday over land, whereas explicit convection produces a peak that is closer to the late afternoon peak seen in observations. At increasing spatial scale, the representation of the diurnal cycle in the explicit and hybrid convection simulations improves when compared to CMORPH; this is not true for any of the parametrized simulations. Some of the strengths and weaknesses of simulated precipitation in a high-resolution GCM are found: the diurnal cycle is improved at all spatial scales with convection parametrization disabled, the interaction of the flow with orography exacerbates existing biases for mean precipitation in the high-resolution simulations, and parametrized simulations produce similar diurnal cycles regardless of their resolution. The need for tuning the high-resolution simulations is made clear. Our approach for evaluating simulated precipitation across a range of scales is widely applicable to other GCMs.

Simulation of Northeast Monsoon in a coupled regional model framework

The rainfall during Northeast Monsoon (NEM) meets most of the water demand and influences the socioeconomic condition of the population in the southern part of India. The present study focuses to identify a better, coupled land-atmosphere combination using regional downscaling experiments with RegCM4 for representing the mean characteristics of the NEM rainfall. Three suites of model integrations are made using three different land-surface schemes such as Biosphere-Atmosphere Transfer Scheme (BATS), Subgrid-BATS (SUB), and Community Land Model version 4.5 (CLM). The CLM experiment shows a better spatial representation of the rainfall due to the better representation of the moisture flux and convection, whereas the BATS and SUB experiments show strong wet bias (1–6 mm/yr) over Tamilnadu and Kerala. The spatial correlation of the climatological rainfall with respect to observation is maximum (0.5–0.6) for the CLM. However, the model experiments fail to reproduce the interannual variability of rainfall. The better representation of climatological rainfall, evaporation and turbulent fluxes (sensible and latent heat flux) leads to an improved temperature climatology in the CLM experiment. The study illustrates that the CLM land-surface scheme performs better in representing the northeast monsoon as compared to others however there is a need to explore avenues for further improvements in the performance of the coupled model framework.

Forecast Skill for California Heavy Precipitation Periods from the High-Resolution Rapid Refresh Model and the Coupled Ocean–Atmosphere Mesoscale Prediction System

Abstract Precipitation forecasts from the High-Resolution Rapid Refresh model (HRRR) of the National Centers for Environmental Prediction (NCEP) and the Navy’s Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) are examined during heavy precipitation periods in California. Precipitation forecast discrepancies between the two models are examined during a recent heavy winter precipitation episode in California from 6 to 8 December 2019. The skill of initial 12-h precipitation forecasts is examined objectively from 1 December 2018 to 28 February 2019 from the HRRR, COAMPS, and NCEP’s North American Mesoscale Forecast System (NAM-3km). The HRRR exhibited lower seasonal biases and higher skill based on several metrics applied to a sample of 48 12-h periods during California’s second wettest winter season during the past 20 years. Overall, the NAM-3km and COAMPS exhibited a large wet bias over the interior mountain regions while the HRRR model indicated a dry bias along the northern coastal region. All models tended to underestimate precipitation along the coastal mountains of Northern California. To highlight the regional and localized nature of forecast skill, the fraction skill score (FSS) metric is applied across ranges of spatial scales and precipitation values. For the domain as a whole, the HRRR had higher precipitation forecast skill compared to the other two models, particularly within radial distances of 20–30 km and moderate (10–50 mm) precipitation totals. FSS computed locally highlights the HRRR’s overall higher skill as well as enhanced skill in the southern half of the state.

MJO propagation over the Indian Ocean and Western Pacific in CMIP5 Models: Roles of Background States

Abstract This study explored the impacts of background states on the propagation of the Madden-Julian Oscillation (MJO) in 24 CMIP5 models using a precipitation-based MJO tracking method. The ability of the model to reproduce the MJO propagation is reflected in the occurrence frequency of individual MJO events. Moisture budget analysis suggests that the occurrence frequencies of MJO events that propagate across the Indian Ocean (IO-MJO) and western Pacific (WP-MJO) in the models are mainly related to the low-level meridional moisture advection ahead of the MJO convection center. This advection is tightly associated with the background distribution of low-level moisture. Drier biases in background low-level moisture over the entire tropical regions account for underestimated MJO occurrence frequency in the bottom-tier simulations. This study highlights the importance of reproducing the year-to-year background states for the simulations of MJO propagation in the models by further decomposing the background states into the climatology and anomaly components. The background meridional moisture gradient account for the IO-MJO occurrence frequency is closely related to its climatology component, however, the anomaly component regulated by the El Niño–Southern Oscillation (ENSO) is also important for the WP-MJO occurrence frequency. The year-to-year variations of background zonal and meridional gradients associated with ENSO account for the IO-MJO occurrence frequency tend to be offset with each other. As a result, the ENSO shows no significant impact on the IO-MJO occurrence frequency. However, the MJO events tend to more likely propagate across the western Pacific during El Niño years.