Atmospheric Forcing Data Sets Research Articles

Southern Weddell Sea surface freshwater flux modulated by icescape and atmospheric forcing

Abstract. Sea ice formation dominates surface salt forcing in the southern Weddell Sea. Brine rejected in the process of sea ice production results in the production of High Salinity Shelf Water (HSSW) that feeds the global overturning circulation and fuels the basal melt of the adjacent ice shelf. The strongest sea ice production rates are found in coastal polynyas, where steady offshore winds promote divergent ice movement during the freezing season. We used the Finite Element Sea ice–ice shelf–Ocean Model (FESOM) forced by output from the regional atmospheric model COSMO-CLM (CCLM) with 14 km horizontal resolution to investigate the role of polynyas for the surface freshwater flux of the southern Weddell Sea (2002–2017). The presence of stationary icescape features (i.e., fast-ice areas and grounded icebergs) can influence the formation of polynyas and, therefore, impact sea ice production. The representation of the icescape in our model is included by prescribing the position, shape and temporal evolution of a largely immobile ice mélange formed between the Filchner–Ronne Ice Shelf (FRIS) and a major grounded iceberg based on satellite data. We find that 70 % of the ice produced on the continental shelf of the southern Weddell Sea is exported from the region. While coastal polynyas cover 2 % of the continental shelf area, sea ice production within the coastal polynyas accounts for 17 % of the overall annual sea ice production (1509 km3). The largest contributions come from the Ronne Ice Shelf and Brunt Ice Shelf polynyas and polynyas associated with the ice mélange. Furthermore, we investigate the sensitivity of the polynya-based ice production to the (i) representation of the icescape and (ii) regional atmospheric forcing. Although large-scale atmospheric fields determine the sea ice production outside polynyas, both the treatment of the icescape and the regional atmospheric forcing are important for the regional patterns of sea ice production in polynyas. The representation of the ice mélange is crucial for the simulation of polynyas westward/eastward of it, which are otherwise suppressed/overestimated. Compared to using ERA-Interim reanalysis as an atmospheric forcing data set, using CCLM output reduces polynya-based ice production over the eastern continental shelf due to weaker offshore winds, yielding a more realistic polynya representation. Our results show that the location and not just the strength of the sea ice production in polynyas is a relevant parameter in setting the properties of the HSSW produced on the continental shelf, which in turn affects the basal melting of the Filchner–Ronne Ice Shelf.

An ultrahigh-resolution E3SM land model simulation framework and its first application to the Seward Peninsula in Alaska

The availability of supercomputers and state-of-science datasets has made it possible to conduct large-scale land simulations at an ultrahigh-resolution. This study reported a computational framework for land surface simulation using the E3SM land model (ELM) at an unprecedented resolution (1 km x 1 km gridcell). The ultrahigh-resolution ELM (uELM) simulation framework includes three parts: (1) high-resolution atmospheric forcing and surface properties dataset generation, (2) massive gridcell-based simulation, and (3) large-scale simulation results analysis. We implemented the uELM simulation framework and completed the first 1 km x 1 km terrestrial ecosystem simulation (from 1850 to 2014) over the Seward Peninsula in Alaska (78,000 km2). The experiment contained two phases: a spin-up simulation and a transient simulation, and required five weeks of calculations using 320 cores in a 44-node Linux HPC computer. It created approximately 1.3 TB of data from the transient simulation alone (1850 - present). We selected sample results (monthly and daily simulation outputs) to illustrate the temporal and spatial variations of several variables in high-latitude Arctic ecosystems’ water, energy, and carbon cycles. At last, we summarized the lessons learned and proposed new developments for full-scale uELM simulations over the entire North American continent (approximately 22,000,000 km2).

Large Uncertainties in Precipitation Exert Considerable Impact on Land Surface Temperature Modeling Over the Tibetan Plateau

AbstractThe performance of land surface temperature (LST) simulations in land surface models is subject to uncertainties in atmospheric forcing datasets. However, the contributions of forcing variables in different datasets to the simulated LST over the Tibetan Plateau (TP) remain unclear. To investigate the sensitivity of LST simulation to atmospheric forcing, we conducted a series of experiments using the Community Land Model (CLM5.0) with different forcing datasets. Results showed that the simulated LST displayed large spatiotemporal variations, and the simulation driven by the China Meteorological Forcing Data set produced more reasonable estimates than the other simulations. Sensitivity analysis suggests that the 1% increase in air temperature causes the largest variation in LST (0.54%), while the same percentage change in precipitation leads to the smallest (only 0.002%) LST variation. However, the actual uncertainty in precipitation is much larger than that in air temperature, and thus uncertainty in precipitation contributes 24% of the LST variability in the cold season, compared to 18% for air temperature. Large precipitation uncertainty leads to strong uncertainties in snow processes (snowfall and snow cover fraction), and hence strong uncertainties in surface albedo and ground absorbed shortwave radiation, further exerting the absolute impact on LST in cold season. Moreover, the snowfall is also influenced by different schemes of snowfall/rainfall partitioning. This may result in uncertainties in snowfall and consequently LST for the southeastern TP. Our study highlights the importance of high‐quality precipitation data, a forcing variable usually ignored in previous studies of LST simulation on the TP.

Characteristics of near‐surface soil freeze–thaw status using high resolution CLM5.0 simulations on the Tibetan Plateau

AbstractSoil freeze–thaw alternation is a natural characteristic of the Tibetan Plateau (TP), and plays an important role in surface energy balance and eco‐hydrological processes. The soil freeze–thaw process on the TP has changed significantly owing to global warming, affecting the alpine ecosystem structure and function. This study used high‐resolution atmospheric forcing datasets to drive the Community Land Model version 5.0 (CLM5.0) to simulate the near‐surface soil freeze–thaw status between 1979 and 2020. The simulated results were compared with in situ observations, and then the spatiotemporal distribution of the freeze start‐date (FSD), freeze end‐date (FED), freeze duration (FD), and thaw duration (TD) at a depth of 0.1 m were analyzed. The Nash–Sutcliffe efficiency coefficients (NSEs) of FSD, FED, FD, and TD between simulations and in situ observations were 0.77, 0.90, 0.98 and 0.92, and the correlation coefficients of FSD, FED, FD, TD were 0.97, 0.99, 0.99 and 0.98, respectively. The spatial distribution of FSD and TD was characterized by gradually increasing from northwest to southeast while FED and FD exhibited the opposite characteristics. FSD, FED, FD, and TD changed at an area‐mean rate of 1.1, −1.4, −2.5, and 2.5 days decade−1, respectively. This study provides an important reference for analyzing and predicting the changes in near surface soil freeze–thaw status on the TP under the warming climate.

An overview of soil moisture drought research in China: Progress and perspective

Soil moisture drought (SMD) directly affects agricultural yield and land water resources. Understanding and predicting the occurrences and evolution of SMD are of great importance for a largely agricultural country such as China. Compared to other drought categories, SMD receives less attention due to the lack of long-term soil moisture datasets. In recent decades, SMD research has been greatly developed in China, benefiting from increased ground and satellite measurements along with state-of-the-art land surface models. Here, the authors provide a brief overview of the recent progress in SMD research in China, focus on historical drought identification and its prediction, and then raise some future perspectives. Based on historical SMD studies, drought frequency has increased overall and drought duration has been prolonged since the 1950s for China as a whole, but they both show substantial temporal variations at the regional scale. Research on SMD prediction has mainly relied on the statistical relationship between soil moisture and climate variables. Few studies based on the dynamical approach in seasonal drought prediction have highlighted the importance of initial conditions and atmospheric forcing datasets. Given the importance of SMD in agricultural practice and water resource management in China, it is necessary to emphasize the following: 1) conducting research on multiple time scales (e.g., from days to the centurial time scale) and cross-regional drought identification research; and 2) developing a SMD prediction system that takes advantage of climate prediction systems, land surface models, and multisource soil moisture datasets.摘要论文回顾了中国土壤湿度干旱 (SMD) 历史重建和季节预测研究进展, 并对未来研究进行了展望. 自1950s年代以来, 全国整体干旱频率增加, 持续时间延长, 且有明显区域特征. SMD预测多是利用土壤湿度与气候变量之间的统计关系, 而少量基于动力学方法的干旱预测研究强调了初始条件和大气强迫数据对季节尺度干旱预测的重要性. 本论文提出: 1) 加强多时间尺度, 跨区域的SMD研究; 2)联合气候预测系统, 陆面模式和多源土壤湿度数据研制SMD预测系统.

Long-term mean circulation in the Japan Sea as reproduced by multiple eddy-resolving ocean circulation models

The capacity of four eddy-resolving ocean circulation models—HYCOM (HYbrid Coordinate Ocean Model), MRI.COM (Meteorological Research Institute Community Ocean Model), OFES (Ocean General Circulation Model for the Earth Simulator), and NEMO (Nucleus for European Modeling of the Ocean)—to simulate the long-term mean hydrographic conditions and circulation patterns in the Japan Sea is investigated in this study. The assessment of this study includes the evaluation of mean vertical profiles and time series of temperature and salinity at the representative monitoring stations. Different model products from 1993 to 2015 are compared with in situ measurements provided by historical cruises and monitoring stations. After that, we compared the observed and simulated surface current velocities over the basin and volume transports through the key straits in the Japan Sea. Simulated current velocities are validated against 15 years of Acoustic Doppler Current Profiler (ADCP) measurements near the longshore and offshore branches of the East Korea Warm Current (EKWC). Furthermore, the atmospheric forcing data of the four ocean circulation models are validated against the satellite wind product. We found that the vertical profiles and long-term variations of temperature and salinity reproduced by MRI.COM and HYCOM are closer to in situ measurements. All models simulate temperature well in upper ocean, but salinity simulations are of lower quality from OFES and NEMO at several stations. Simulated current velocities predominantly lie within the standard deviation of ADCP measurements at two locations. However, the sea surface currents are underestimated by four models compared with Drifter data. Although simulated hydrographic profiles agree well with in situ observations, the mean circulation patterns greatly differ between the models, which highlight the need for additional evaluation and corrections based on the long-term current measurements. Because of the lack of ocean current measurements, only the baroclinic velocities simulated by each model are reliable. The substantial part of the differences in barotropic velocities among the simulate result of four models is explained by the differing wind velocities from the corresponding atmospheric forcing datasets.

Uncertainty in land carbon budget simulated by terrestrial biosphere models: the role of atmospheric forcing

Global estimates of the land carbon sink are often based on simulations by terrestrial biosphere models (TBMs). The use of a large number of models that differ in their underlying hypotheses, structure and parameters is one way to assess the uncertainty in the historical land carbon sink. Here we show that the atmospheric forcing datasets used to drive these TBMs represent a significant source of uncertainty that is currently not systematically accounted for in land carbon cycle evaluations. We present results from three TBMs each forced with three different historical atmospheric forcing reconstructions over the period 1850–2015. We perform an analysis of variance to quantify the relative uncertainty in carbon fluxes arising from the models themselves, atmospheric forcing, and model-forcing interactions. We find that atmospheric forcing in this set of simulations plays a dominant role on uncertainties in global gross primary productivity (GPP) (75% of variability) and autotrophic respiration (90%), and a significant but reduced role on net primary productivity and heterotrophic respiration (30%). Atmospheric forcing is the dominant driver (52%) of variability for the net ecosystem exchange flux, defined as the difference between GPP and respiration (both autotrophic and heterotrophic respiration). In contrast, for wildfire-driven carbon emissions model uncertainties dominate and, as a result, model uncertainties dominate for net ecosystem productivity. At regional scales, the contribution of atmospheric forcing to uncertainty shows a very heterogeneous pattern and is smaller on average than at the global scale. We find that this difference in the relative importance of forcing uncertainty between global and regional scales is related to large differences in regional model flux estimates, which partially offset each other when integrated globally, while the flux differences driven by forcing are mainly consistent across the world and therefore add up to a larger fractional contribution to global uncertainty.

Intercomparison of atmospheric forcing datasets and two PBL schemes for precipitation modelling over a coastal valley in northern British Columbia, Canada

AbstractEnvironmental modelling of remote areas requires dynamical downscaling of meteorological data to obtain precipitation values that could substitute for sparse in‐situ observations. This study examined numerical simulations of precipitation over the Terrace‐Kitimat Valley, an industrializing corridor in the Coast Mountains of northern British Columbia, Canada. Modelling uncertainty was explored for 1 year of output from the Weather Research and Forecasting model at 1‐km grid spacing for three atmospheric forcing datasets and two planetary boundary layer (PBL) schemes. The observed total precipitation ranged from 1170 to 2380 mm and was often underestimated by more than 40% when using the North American Regional Reanalysis as atmospheric forcing data or the Mellor‐Yamada‐Nakanishi‐Niino level 3 (MYNN3) parameterization as PBL scheme. Persistent low bias from model configurations using these configurations suggested that merely selecting an alternative atmospheric forcing dataset does not ameliorate systematic error occasioned by a poorly performing PBL parameterization. Hence, the choice of the PBL scheme and the meteorological dataset is important for spatial estimation of precipitation using WRF. Model output best corresponded with annual gauge measurements when simulations with the Mellor‐Yamada‐Janjić (MYJ) PBL scheme were forced with ERA5. The North American Mesoscale Analyses (NAM‐ANL) however demonstrated better performance for monthly variation and high‐intensity precipitation than ERA5. Using both datasets therefore may be valuable for calculations related to environmental change. With either NAM‐ANL or ERA5 as atmospheric forcing data and MYJ as the PBL scheme, the uncertainty in annual simulated precipitation amount ranged between 38% overestimation and 21% underestimation of observational data.

Ice Algae Model Intercomparison Project phase 2 (IAMIP2)

Abstract. Ice algae play a fundamental role in shaping sea-ice-associated ecosystems and biogeochemistry. This role can be investigated by field observations; however the influence of ice algae at the regional and global scales remains unclear due to limited spatial and temporal coverage of observations and because ice algae are typically not included in current Earth system models. To address this knowledge gap, we introduce a new model intercomparison project (MIP), referred to here as the Ice Algae Model Intercomparison Project phase 2 (IAMIP2). IAMIP2 is built upon the experience from its previous phase and expands its scope to global coverage (both Arctic and Antarctic) and centennial timescales (spanning the mid-20th century to the end of the 21st century). Participating models are three-dimensional regional and global coupled sea-ice–ocean models that incorporate sea-ice ecosystem components. These models are driven by the same initial conditions and atmospheric forcing datasets by incorporating and expanding the protocols of the Ocean Model Intercomparison Project, an endorsed MIP of the Coupled Model Intercomparison Project phase 6 (CMIP6). Doing so provides more robust estimates of model bias and uncertainty and consequently advances the science of polar marine ecosystems and biogeochemistry. A diagnostic protocol is designed to enhance the reusability of the model data products of IAMIP2. Lastly, the limitations and strengths of IAMIP2 are discussed in the context of prospective research outcomes.

SITool (v1.0) – a new evaluation tool for large-scale sea ice simulations: application to CMIP6 OMIP

Abstract. The Sea Ice Evaluation Tool (SITool) described in this paper is a performance metrics and diagnostics tool developed to evaluate the skill of Arctic and Antarctic model reconstructions of sea ice concentration, extent, edge location, drift, thickness, and snow depth. It is a Python-based software and consists of well-documented functions used to derive various sea ice metrics and diagnostics. Here, SITool version 1.0 (v1.0) is introduced and documented, and is then used to evaluate the performance of global sea ice reconstructions from nine models that provided sea ice output under the experimental protocols of the Coupled Model Intercomparison Project phase 6 (CMIP6) Ocean Model Intercomparison Project with two different atmospheric forcing datasets: the Coordinated Ocean-ice Reference Experiments version 2 (CORE-II) and the updated Japanese 55-year atmospheric reanalysis (JRA55-do). Two sets of observational references for the sea ice concentration, thickness, snow depth, and ice drift are systematically used to reflect the impact of observational uncertainty on model performance. Based on available model outputs and observational references, the ice concentration, extent, and edge location during 1980–2007, as well as the ice thickness, snow depth, and ice drift during 2003–2007 are evaluated. In general, model biases are larger than observational uncertainties, and model performance is primarily consistent compared to different observational references. By changing the atmospheric forcing from CORE-II to JRA55-do reanalysis data, the overall performance (mean state, interannual variability, and trend) of the simulated sea ice areal properties in both hemispheres, as well as the mean ice thickness simulation in the Antarctic, the mean snow depth, and ice drift simulations in both hemispheres are improved. The simulated sea ice areal properties are also improved in the model with higher spatial resolution. For the cross-metric analysis, there is no link between the performance in one variable and the performance in another. SITool is an open-access version-controlled software that can run on a wide range of CMIP6-compliant sea ice outputs. The current version of SITool (v1.0) is primarily developed to evaluate atmosphere-forced simulations and it could be eventually extended to fully coupled models.

Aerosol-induced direct radiative forcing effects on terrestrial ecosystem carbon fluxes over China

Atmospheric aerosols can change vegetation photosynthesis through the effects of aerosols on radiation, which will affect the peak carbon dioxide emissions and carbon neutrality at global scales. In this study, we quantify the aerosol-induced direct radiation forcing (ADRF) in China from 2001 to 2014 based on the radiation flux simulation used by the Fu-Liou radiation transfer model under with-aerosols and no-aerosols scenarios. Using the radiation simulation results, we modify the atmospheric forcing datasets to drive Community Land Model 4.5 (CLM4.5) to gain the changes in carbon fluxes in China caused by ADRF. The results show that these two models are accurate in estimating radiation (R2 = 0.78–0.88) and carbon fluxes (R2 = 0.73–0.75) in China. High levels of ADRFs were captured in China, especially with increasing diffuse fraction, resulting in the diffusing fertilization effect occurring in most areas of China. The ADRF can increase cumulative gross primary productivity (GPP) and total ecosystem respiration (ER) by 3.20 gC m−2 and 5.13 gC m−2 per year, respectively. From 2001 to 2014, the diffusing fertilization effects experienced trends of increasing first and then decreasing. However, ADRFs in some regions of China show negative effects on carbon fluxes due to vulnerable vegetation functional types and high aerosol loading. The ADRF will also enable soil temperature decreases and volumetric soil water increases, which is closely related to changes in carbon fluxes. Meanwhile, due to changes in soil water and heat conditions, N2O and CH4 production will also be disturbed, and ADRF increases the global warming potential (GWP) for both greenhouse gases. This phenomenon indicated that atmospheric aerosol pollution control is far-reaching significance for peaking carbon dioxide emissions before 2030.

G‐RUN ENSEMBLE: A Multi‐Forcing Observation‐Based Global Runoff Reanalysis

AbstractRiver discharge is an Essential Climate Variable (ECV) and is one of the best monitored components of the terrestrial water cycle. Nonetheless, gauging stations are distributed unevenly around the world, leaving many white spaces on global freshwater resources maps. Here, we use a machine learning algorithm and historical weather data to upscale sparse in situ river discharge measurements. We provide a global reanalysis of monthly runoff rates for periods covering decades to the past century at a resolution of 0.5° (about 55 km), and with up to 525 ensemble members based on 21 different atmospheric forcing data sets. This global runoff reconstruction, named Global RUNoff ENSEMBLE (G‐RUN ENSEMBLE), is evaluated using independent observations from large river basins and benchmarked against other publicly available runoff data sets over the period 1981–2010. The accuracy of the data set is evaluated on observed river flow from basins not used for model calibration and is found to compare favorably against state‐of‐the‐art global hydrological model simulations. The G‐RUN ENSEMBLE estimates the global mean runoff volume to range between 3.2 × 104 and 3.8 × 104 km3 yr−1. This publicly available data set (https://doi.org/10.6084/m9.figshare.12794075) has a wide range of applications, including regional water resources assessments, climate change attribution studies, hydro‐climatic process studies as well as the evaluation, calibration and refinement of global hydrological models.

Evaluating and improving simulations of diurnal variation in land surface temperature with the Community Land Model for the Tibetan Plateau.

This study evaluated and improved the ability of the Community Land Model version 5.0 (CLM5.0) in simulating the diurnal land surface temperature (LST) cycle for the whole Tibetan Plateau (TP) by comparing it with Moderate Resolution Imaging Spectroradiometer satellite observations. During daytime, the model underestimated the LST on sparsely vegetated areas in summer, whereas cold biases occurred over the whole TP in winter. The lower simulated daytime LST resulted from weaker heat transfer resistances and greater soil thermal conductivity in the model, which generated a stronger heat flux transferred to the deep soil. During nighttime, CLM5.0 overestimated LST for the whole TP in both two seasons. These warm biases were mainly due to the greater soil thermal inertia, which is also related to greater soil thermal conductivity and wetter surface soil layer in the model. We employed the sensible heat roughness length scheme from Zeng, Wang & Wang (2012), the recommended soil thermal conductivity scheme from Dai et al. (2019), and the modified soil evaporation resistance parameterization, which was appropriate for the TP soil texture, to improve simulated daytime and nighttime LST, evapotranspiration, and surface (0–10 cm) soil moisture. In addition, the model produced lower daytime LST in winter because of overestimation of the snow cover fraction and an inaccurate atmospheric forcing dataset in the northwestern TP. In summary, this study reveals the reasons for biases when simulating LST variation, improves the simulations of turbulent fluxes and LST, and further shows that satellite-based observations can help enhance the land surface model parameterization and unobservable land surface processes on the TP.

Towards hyper-resolution land-surface modeling of surface and root zone soil moisture

The goal of this work is to estimate surface and root zone soil moisture at resolutions that are useful for decision making and water resources management. A 500-m atmospheric forcing dataset is developed from the 12.5-km NLDAS-2 (North America Land Data Assimilation System) products across Oklahoma, where high-quality observations are available for validation purposes. A land surface model is then forced with three combinations of input variables to simulate surface and root zone soil moisture: 1) NLDAS-2 atmospheric forcings at their original resolution; 2) downscaled NLDAS-2 atmospheric variables (i.e., near-surface air temperature and humidity, wind speed and direction, incident longwave and shortwave radiation, pressure) and original resolution NLDAS-2 precipitation; and 3) downscaled NLDAS-2 atmospheric variables and precipitation. Results show that the third simulation is able to bring modeled standard-normal deviates of both surface and root zone soil moisture closer to in-situ observations, whereas the second simulation only shows slight improvements with respect to one forced with original resolution NLDAS-2 data. This is particularly evident for negative values of standard-normal deviates, which correspond to drier than usual cases, due to the improved ability of the downscaled precipitation to detect missed events and no-rain cases. In summary, finer resolution forcings have the potential to improve simulations of soil moisture and the resolution of precipitation plays a critical role in improving time series of soil moisture standard-normal deviates.

Sensitivity of Snow Simulations to Different Atmospheric Forcing Data Sets in the Land Surface Model CAS‐LSM

AbstractThe quality of snow simulations in land surface models (LSMs) largely depends on the accuracy of the atmospheric forcing data, especially precipitation and air temperature. To investigate the sensitivities of snow simulations to atmosphere forcing, historical simulations from 1981–2010 were conducted using the Chinese Academy of Sciences land surface model (CAS‐LSM) with four atmospheric forcing data sets: third Global Soil Wetness Projects (GSWP3), the Water and Global Change (WATCH) Forcing Data (WFD/WFDEI), the Climate Research Unit ‐ National Centers for Environmental Prediction (CRU‐NCEP), and Princeton. A sensitivity index (Ψ) is utilized to quantify the sensitivity of the simulated snow cover fraction (SCF), snow water equivalent (SWE), and snow depth (SDP) to the uncertainties in forcing data. By comparing the simulated results with satellite‐based products and in situ observations, we find that CAS‐LSM generally captured the spatial and seasonal variations of the SCF, SWE, and SDP. The simulation based on GSWP3 produced more reasonable estimates than the other three simulations, particularly for the SCF and SDP. The sensitivity analysis suggested that the SWE and SDP suffered the most from the uncertainties in the atmospheric forcing data sets. The sensitivities of the SCF, SWE, and SDP to precipitation uncertainties had various seasonal cycles depending on the climate regime. The highest sensitivity in boreal climates appeared during January–March, while in warm temperate climates the highest sensitivity existed during April–June. On average, the strongest precipitation sensitivity was found for temperate climates with cold, dry winters. The sensitivities to uncertainties in air temperature showed similar patterns; however, air temperature sensitivity was generally dominant over precipitation sensitivity in boreal climates, while in warm temperate climates both of them were high.

The impacts of late spring soil moisture in the Tibetan Plateau on summer precipitation in eastern China

AbstractThe Community Land Model version 4.5(CLM4.5), driven by the atmospheric forcing dataset CRUNCEP, was used to simulate the spatial and temporal characteristics of soil moisture across the Tibetan Plateau from 1981 to 2016. Compared with station data and two reanalysis products (i.e., the ERA‐Interim and GLDAS‐CLM), it is found that CLM4.5 can well reproduce the spatial and temporal variations of soil moisture in the Tibetan Plateau. This study reveals the relationship between soil moisture on the Tibetan Plateau in May and summer precipitation in eastern China and the physical mechanism for the impact of soil moisture on summer precipitation. Singular value decomposition (SVD)analysis shows that the surface soil moisture in the south‐central Tibetan Plateau was positively related to the summer precipitation in South China and negatively related to that in the middle and lower reaches of the Yangtze River and Northeast China. The soil moisture in the western Tibetan Plateau was the opposite of that in the south‐central region. The wetter the surface soil moisture in the south‐central Tibetan Plateau in May was, the lower the surface temperature, sensible heat flux and net longwave radiation flux, and the higher the latent heat flux and net shortwave radiation flux and leading to weaker surface heating. In contrast, lower surface soil moisture in the western Tibetan Plateau led to stronger surface heating. This led to a weaker western Pacific subtropical high and a more northerly rain belt. The weather in South China was controlled by large cyclonic circulations forming convergences and updrafts that led to more rainfall in South China. The precipitation in the middle and lower reaches of the Yangtze River and Northeast China was less under the control of the anomalous anticyclone than that in South China, and vice versa.

Roles of atmospheric variabilities in the formation of the Indian Ocean Dipole

Roles of atmospheric variabilities in the formation of Indian Ocean Dipole (IOD) were investigated using an ocean general circulation model and different atmospheric forcing datasets. Simulated IODs indicated that IOD amplitude decreased without intraseasonal variability and the sea surface temperature anomaly (SSTA) peaks moved northward, resulting in unrealistic SSTA patterns. Analysis on the tendencies contributing to forming the SSTA of IODs revealed that the oceanic vertical processes were mainly influenced by seasonal variability, which was related to El Nino (La Nina) through anticyclonic (cyclonic) surface wind circulation and equatorial Kelvin waves in the Indian Ocean. Madden-Julian oscillations (MJOs) were found to have large impacts on the change in latent heat flux and surface wind anomaly that controlled the SSTA. In the wake region of MJOs, surface easterly (westerly) wind anomalies were magnified due to the suppressed (enhanced) convection phase of the MJO, whereas the effect of the opposite phase was weakened by the preceding anticyclonic (cyclonic) sea level pressure for positive IODs (negative IODs). Further, the MJO modulated meridional gradient of sea level pressure anomaly, and the resulting effect changed the locations of SSTAs.

High-resolution simulation and validation of soil moisture in the arid region of Northwest China

Soil moisture plays an important role in land-atmosphere interactions, agricultural drought monitoring, and water resource management, particularly across arid regions. However, it is challenging to simulate soil moisture of high spatial resolution and to evaluate soil moisture at fine spatial resolution in arid regions in Northwest China due to considerable uncertainties in forcing data and limited in situ measurements. Then, the data set was used to produce the 1 km high-resolution atmospheric forcing datasets and to drive the Community Land Model version 3.5 (CLM3.5) for simulating spatiotemporally continuous surface soil moisture. The capabilities of soil moisture simulation using CLM3.5 forced by the XJLDAS-driven field were validated against data obtained at three soil layers (0–10, 0–20, and 0–50 cm) from 54 soil moisture stations in Xinjiang. Results show that the simulated soil moisture agreed well with the observations [CORR > 0.952], and the intra-annual soil moisture in Xinjiang gradually increased during May through August. The main factors that affect changes in soil moisture across the study region were precipitation and snowmelt. The overall finding of this study is that an XJLDAS, high-resolution forcing data driven CLM3.5 can be used to generate accurate and continuous soil moisture of high resolution (1km) in Xinjiang. This study can help understand the spatiotemporal features of the soil moisture, and provide important input for hydrological studies and agricultural water resources management over the arid region.

Grid size dependency of short-term sea ice forecast and its evaluation during extreme Arctic cyclone in August 2016

Recently, cyclones are quite active and dramatically influence the sea ice distribution in the Arctic region. Therefore, precise prediction of sea ice distribution during the cyclones is crucial for safe and efficient navigation in the Arctic Ocean. A high-resolution (about 2.5 km) ice–ocean coupled model is developed for forecasting the short-term sea ice distribution along the Arctic sea routes. Since a commercial ship navigates to avoid the sea ice as much as possible, a factor to score the forecast skill is considered to be ice edge error which is an averaged distance between forecasted and observed ice edges. First, we discuss the influence of horizontal grid resolution on short-term sea ice predictions during May to November 2015. The model grid resolution is varied from 2.5 km, 5 km, 10 km and 15 km. This grid dependency study suggests that the 2.5 km resolution model predicts the ice edge accurately compared to the satellite observations. Second, the primary experiment was run from 2 August 2016 to 7 August 2016 under the extreme cyclone in the Laptev Sea using ensemble predictions. The ensembles are constructed by using forecasted atmospheric forcing data sets from THORPEX Interactive Grand Global Ensemble (TIGGE) project and European Center for Medium-Range Weather Forecast Interim (ERA-Interim) reanalysis data. The ensemble ice edge errors are computed from 2 August 2016 to 7 August 2016. The maximum forecast skill of ensemble average ice edge error in the ice–ocean coupled model is 11.74 ± 0.54 km with the threshold of 15% ice concentration for the AMSR2 and model predicted ice edges. It can be said that the present model of 2.5 km grids satisfies the ship crew requirement of around 10 km ice edge error for 5-day forecast.

An Evaluation of the Ocean and Sea Ice Climate of E3SM Using MPAS and Interannual CORE‐II Forcing

AbstractThe Energy Exascale Earth System Model (E3SM) is a new coupled Earth system model sponsored by the U.S Department of Energy. Here we present E3SM global simulations using active ocean and sea ice that are driven by the Coordinated Ocean‐ice Reference Experiments II (CORE‐II) interannual atmospheric forcing data set. The E3SM ocean and sea ice components are MPAS‐Ocean and MPAS‐Seaice, which use the Model for Prediction Across Scales (MPAS) framework and run on unstructured horizontal meshes. For this study, grid cells vary from 30 to 60 km for the low‐resolution mesh and 6 to 18 km at high resolution. The vertical grid is a structured z‐star coordinate and uses 60 and 80 layers for low and high resolution, respectively. The lower‐resolution simulation was run for five CORE cycles (310 years) with little drift in sea surface temperature (SST) or heat content. The meridional heat transport (MHT) is within observational range, while the meridional overturning circulation at 26.5°N is low compared to observations. The largest temperature biases occur in the Labrador Sea and western boundary currents (WBCs), and the mixed layer is deeper than observations at northern high latitudes in the winter months. In the Antarctic, maximum mixed layer depths (MLD) compare well with observations, but the spatial MLD pattern is shifted relative to observations. Sea ice extent, volume, and concentration agree well with observations. At high resolution, the sea surface height compares well with satellite observations in mean and variability.