Global Climate Prediction Research Articles

CESM/CAM5 improvement and application: comparison and evaluation of updated CB05_GE and MOZART-4 gas-phase mechanisms and associated impacts on global air quality and climate

Abstract. Atmospheric chemistry plays a key role in determining the amounts and distributions of oxidants and gaseous precursors that control the formation of secondary gaseous and aerosol pollutants; all of those species can interact with the climate system. To understand the impacts of different gas-phase mechanisms on global air quality and climate predictions, in this work, a comprehensive comparative evaluation is performed using the Community Atmosphere Model (CAM) Version 5 with comprehensive tropospheric and stratospheric chemistry (CAM5-chem) within the Community Earth System Model (CESM) with the two most commonly used gas-phase chemical mechanisms: the 2005 Carbon Bond mechanism with Global Extension (CB05_GE) and the Model of OZone and Related chemical Tracers version 4 (MOZART-4) mechanism with additional updates (MOZART-4x). MOZART-4x and CB05_GE use different approaches to represent volatile organic compounds (VOCs) and different surrogates for secondary organic aerosol (SOA) precursors. MOZART-4x includes a more detailed representation of isoprene chemistry compared to CB05_GE. CB05_GE includes additional oxidation of SO2 by O3 over the surface of dust particles, which is not included in MOZART-4x. The results show that the two CAM5-chem simulations with CB05_GE and MOZART-4x predict similar chemical profiles for major gases (e.g., O3, CO, and NOx) compared to the aircraft measurements, with generally better agreement for NOy profiles by CB05_GE than MOZART-4x. The concentrations of SOA at four sites in the continental US (CONUS) and organic carbon (OC) over the IMPROVE sites are well predicted by MOZART-4x (with normalized mean biases (NMBs) of −1.9 and 2.1 %, respectively) but moderately underpredicted by CB05_GE (with NMBs of −23.1 and −20.7 %, respectively). This is mainly due to the higher biogenic emissions and OH levels simulated with MOZART-4x than with CB05_GE. The concentrations of OC over Europe are largely underpredicted by both MOZART-4x and CB05_GE, with NMBs of −73.0 and −75.1 %, respectively, indicating the uncertainties in the emissions of precursors and primary OC and relevant model treatments such as the oxidations of VOCs and SOA formation. Uncertainties in the emissions and convection scheme can contribute to the large bias in the model predictions (e.g., SO2, CO, black carbon, and aerosol optical depth). The two simulations also have similar cloud/radiative predictions, with a slightly better performance of domain average cloud condensation nuclei (CCN) at supersaturation of 0.5 % by CB05_GE, but slightly better agreement with observed CCN (at supersaturation of 0.2 %) profile over Beijing by MOZART-4x. The two gas-phase mechanisms result in a global average difference of 0.5 W m−2 in simulated shortwave cloud radiative forcing, with significant differences (e.g., up to 13.6 W m−2) over subtropical regions.

Putting the clouds back in aerosol–cloud interactions

Abstract. Aerosol–cloud interactions (ACI) are the consequence of perturbed aerosols affecting cloud drop and crystal number, with corresponding microphysical and radiative effects. ACI are sensitive to both cloud microphysical processes (the "C" in ACI) and aerosol emissions and processes (the "A" in ACI). This work highlights the importance of cloud microphysical processes, using idealized and global tests of a cloud microphysics scheme used for global climate prediction. Uncertainties in key cloud microphysical processes examined with sensitivity tests cause uncertainties of nearly −30 to +60 % in ACI, similar to or stronger than uncertainties identified due to natural aerosol emissions (−30 to +30 %). The different dimensions and sensitivities of ACI to microphysical processes identified in previous work are analyzed in detail, showing that precipitation processes are critical for understanding ACI and that uncertain cloud lifetime effects are nearly one-third of simulated ACI. Buffering of different processes is important, as is the mixed phase and coupling of the microphysics to the condensation and turbulence schemes in the model.

Long‐term projections of temperature, precipitation and soil moisture using non‐stationary oscillation processes over the UAE region

ABSTRACTThis study discusses the evolution of temperature, precipitation and soil moisture patterns over the United Arab Emirates (UAE) region, which is characterized by hot climate and scarce precipitation. A stochastic model that reproduces non‐stationary oscillation (NSO) processes by utilizing ensemble empirical mode decomposition (EEMD) and non‐parametric techniques is used to predict the evolution of temperature, precipitation and soil moisture. The long‐term gridded temperature, precipitation and soil moisture data from the Global Historic Climatic Network, Global Precipitation Climatology Center and Climate Prediction Center are used in this study. The data consists of 65 years of average monthly temperature and soil moisture measurements and 110 years of average monthly precipitation over the UAE. The last 20 years of observations of temperature, precipitation and soil moisture are reserved for the validation of the methodology and the rest of the data is used for prediction. The results show that future long‐term patterns are well captured by the model and hence confirm the potential of the EEMD technique and the NSO resampling (NSOR) modelling process. The model is also used for forecasting the evolution of temperature, precipitation and soil moisture patterns for the next 30 years. This procedure is finally used to produce the spatial patterns of temperature, precipitation and soil moisture. Significant increase in temperature and decrease in precipitation and soil moisture are observed particularly over Abu Dhabi. The spatial map shows strong increase (decrease) in temperature (precipitation and soil moisture) over most of the UAE. The results are quite different for the south eastern part of the UAE. Western parts of the UAE are projected to see larger temperature increases than other parts. The results are coherent with the previous findings over this region.

Decadal simulation and comprehensive evaluation of CESM/CAM5.1 with advanced chemistry, aerosol microphysics, and aerosol‐cloud interactions

AbstractEarth system models have been used for climate predictions in recent years due to their capabilities to include biogeochemical cycles, human impacts, as well as coupled and interactive representations of Earth system components (e.g., atmosphere, ocean, land, and sea ice). In this work, the Community Earth System Model (CESM) with advanced chemistry and aerosol treatments, referred to as CESM‐NCSU, is applied for decadal (2001–2010) global climate predictions. A comprehensive evaluation is performed focusing on the atmospheric component—the Community Atmosphere Model version 5.1 (CAM5.1) by comparing simulation results with observations/reanalysis data and CESM ensemble simulations from the Coupled Model Intercomparison Project phase 5 (CMIP5). The improved model can predict most meteorological and radiative variables relatively well with normalized mean biases (NMBs) of −14.1 to −9.7% and 0.7–10.8%, respectively, although temperature at 2 m (T2) is slightly underpredicted. Cloud variables such as cloud fraction (CF) and precipitating water vapor (PWV) are well predicted, with NMBs of −10.5 to 0.4%, whereas cloud condensation nuclei (CCN), cloud liquid water path (LWP), and cloud optical thickness (COT) are moderately‐to‐largely underpredicted, with NMBs of −82.2 to −31.2%, and cloud droplet number concentration (CDNC) is overpredictd by 26.7%. These biases indicate the limitations and uncertainties associated with cloud microphysics (e.g., resolved clouds and subgrid‐scale cumulus clouds). Chemical concentrations over the continental U.S. (CONUS) (e.g., , Cl−, OC, and PM2.5) are reasonably well predicted with NMBs of −12.8 to −1.18%. Concentrations of SO2, , and PM10 are also reasonably well predicted over Europe with NMBs of −20.8 to −5.2%, so are predictions of SO2 concentrations over the East Asia with an NMB of −18.2%, and the tropospheric ozone residual (TOR) over the globe with an NMB of −3.5%. Most meteorological and radiative variables predicted by CESM‐NCSU agree well overall with those predicted by CESM‐CMIP5. The performance of LWP and AOD predicted by CESM‐NCSU is better than that of CESM‐CMIP5 in terms of model bias and correlation coefficients. Large biases for some chemical predictions can be attributed to uncertainties in the emissions of precursor gases (e.g., SO2, NH3, and NOx) and primary aerosols (black carbon and primary organic matter) as well as uncertainties in formulations of some model components (e.g., online dust and sea‐salt emissions, secondary organic aerosol formation, and cloud microphysics). Comparisons of CESM simulation with baseline emissions and 20% of anthropogenic emissions from the baseline emissions indicate that anthropogenic gas and aerosol species can decrease downwelling shortwave radiation (FSDS) by 4.7 W m−2 (or by 2.9%) and increase SWCF by 3.2 W m−2 (or by 3.1%) in the global mean.

Evaluation of Historical and Future Cool Season Precipitation over the Eastern United States and Western Atlantic Storm Track Using CMIP5 Models

Abstract This study analyzed the contribution of cyclones to projected changes in cool season (1 November–31 March) precipitation over the eastern United States and western North Atlantic Ocean. First, global climate model simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) were compared to Global Precipitation Climatology Project (GPCP) and Climate Prediction Center (CPC) precipitation analyses for the period 1979–2004. The CMIP5 ensemble mean realistically reproduced the historical distribution of regional precipitation with no discernable effect because of model spatial resolution. Subsequently, the projected changes in precipitation on cyclone and noncyclone days under the representative concentration pathway 8.5 (RCP8.5) scenario were quantified. While precipitation on both types of days was projected to increase, the increase on noncyclone days (23%) was greater than the increase on cyclone days (12%). The increase in precipitation on cyclone days occurred despite a decrease in the number of cyclone days. This increase can be attributed primarily to a shift toward more frequent extreme precipitation events coupled with a decline in light precipitation events.

Combined effects of the Pacific Decadal Oscillation and El Niño-Southern Oscillation on global land dry-wet changes.

The effects of natural variability, especially El Niño-Southern Oscillation (ENSO) effects, have been the focus of several recent studies on the change of drought patterns with climate change. The interannual relationship between ENSO and the global climate is not stationary and can be modulated by the Pacific Decadal Oscillation (PDO). However, the global land distribution of the dry–wet changes associated with the combination of ENSO and the PDO remains unclear. In the present study, this is investigated using a revised Palmer Drought Severity Index dataset (sc_PDSI_pm). We find that the effect of ENSO on dry–wet changes varies with the PDO phase. When in phase with the PDO, ENSO-induced dry–wet changes are magnified with respect to the canonical pattern. When out of phase, these dry–wet variations weaken or even disappear. This remarkable contrast in ENSO's influence between the two phases of the PDO highlights exciting new avenues for obtaining improved global climate predictions. In recent decades, the PDO has turned negative with more La Niña events, implying more rain and flooding over land. La Niña-induced wet areas become wetter and the dry areas become drier and smaller due to the effects of the cold PDO phase.

Design and testing of a global climate prediction system based on a coupled climate model

A global climate prediction system (PCCSM4) was developed based on the Community Climate System Model, version 4.0, developed by the National Center for Atmospheric Research (NCAR), and an initialization scheme was designed by our group. Thirty-year (1981–2010) one-month-lead retrospective summer climate ensemble predictions were carried out and analyzed. The results showed that PCCSM4 can efficiently capture the main characteristics of JJA mean sea surface temperature (SST), sea level pressure (SLP), and precipitation. The prediction skill for SST is high, especially over the central and eastern Pacific where the influence of El Nino-Southern Oscillation (ENSO) is dominant. Temporal correlation coefficients between the predicted Nino3.4 index and observed Nino3.4 index over the 30 years reach 0.7, exceeding the 99% statistical significance level. The prediction of 500-hPa geopotential height, 850-hPa zonal wind and SLP shows greater skill than for precipitation. Overall, the predictability in PCCSM4 is much higher in the tropics than in global terms, or over East Asia. Furthermore, PCCSM4 can simulate the summer climate in typical ENSO years and the interannual variability of the Asian summer monsoon well. These preliminary results suggest that PCCSM4 can be applied to real-time prediction after further testing and improvement.

Massively parallel solvers for elliptic partial differential equations in numerical weather and climate prediction

The demand for substantial increases in the spatial resolution of global weather and climate prediction models makes it necessary to use numerically efficient and highly scalable algorithms to solve the equations of large‐scale atmospheric fluid dynamics. For stability and efficiency reasons, several of the operational forecasting centres, in particular the Met Office and the European Centre for Medium‐Range Weather Forecasts (ECMWF) in the UK, use semi‐implicit semi‐Lagrangian time‐stepping in the dynamical core of the model. The additional burden with this approach is that a three‐dimensional elliptic partial differential equation (PDE) for the pressure correction has to be solved at every model time step and this often constitutes a significant proportion of the time spent in the dynamical core. In global models, this PDE must be solved in a thin spherical shell.To run within tight operational time‐scales, the solver has to be parallelized and there seems to be a (perceived) misconception that elliptic solvers do not scale to large processor counts and hence implicit time‐stepping cannot be used in very high‐resolution global models. After reviewing several methods for solving the elliptic PDE for the pressure correction and their application in atmospheric models, we demonstrate the performance and very good scalability of Krylov subspace solvers and multigrid algorithms for a representative model equation with more than 1010 unknowns on 65 536 cores on the High‐End Computing Terascale Resource (HECToR), the UK's national supercomputer. For this, we tested and optimized solvers from two existing numerical libraries (the Distributed and Unified Numerics Environment (DUNE) and Parallel High Performance Preconditioners (hypre)) and implemented both a conjugate gradient solver and a geometric multigrid algorithm based on a tensor‐product approach, which exploits the strong vertical anisotropy of the discretized equation. We study both weak and strong scalability and compare the absolute solution times for all methods; in contrast to one‐level methods, the multigrid solver is robust with respect to parameter variations.

The gravity wave momentum flux in hydrostatic flow with directional shear over elliptical mountains

Semi-analytical expressions for the momentum flux associated with orographic internal gravity waves, and closed analytical expressions for its divergence, are derived for inviscid, stationary, hydrostatic, directionally-sheared flow over mountains with an elliptical horizontal cross-section. These calculations, obtained using linear theory conjugated with a third-order WKB approximation, are valid for relatively slowly-varying, but otherwise generic wind profiles, and given in a form that is straightforward to implement in drag parametrization schemes. When normalized by the surface drag in the absence of shear, a quantity that is calculated routinely in existing drag parametrizations, the momentum flux becomes independent of the detailed shape of the orography. Unlike linear theory in the Ri→∞ limit, the present calculations account for shear-induced amplification or reduction of the surface drag, and partial absorption of the wave momentum flux at critical levels. Profiles of the normalized momentum fluxes obtained using this model and a linear numerical model without the WKB approximation are evaluated and compared for two idealized wind profiles with directional shear, for different Richardson numbers (Ri). Agreement is found to be excellent for the first wind profile (where one of the wind components varies linearly) down to Ri=0.5, while not so satisfactory, but still showing a large improvement relative to the Ri→∞ limit, for the second wind profile (where the wind turns with height at a constant rate keeping a constant magnitude). These results are complementary, in the Ri⪆O(1) parameter range, to Broad’s generalization of the Eliassen–Palm theorem to 3D flow. They should contribute to improve drag parametrizations used in global weather and climate prediction models.

FY-3 Meteorological Satellites and the Applications

FY-3 is the second generation polar-orbiting meteorological satellite of China. The first satellite named FY-3A of this series was launched on 27 May 2008. The first operational satellite named FY-3C of this series was launched on 23 September, 2013. The new generation satellites are to provide three-dimensional, quantitative, multi-spectral global remote sensing data under all weather conditions, which will greatly help the operational numerical weather prediction, global climate change research, climate diagnostics and prediction, and natural disaster monitoring. They will also provide help for many other fields such as agriculture, forestry, oceanography and hydrology. With the abovementioned capability, the FY-3 satellites can make valuable contributions to improving weather forecasts, global natural-disaster and environmental monitoring.

Standardizing Air Chemistry

A recent United Nations report on global warming includes more accurate and reliable climate predictions than ever before, and its authors give credit to recent improvements in the fundamental understanding of climate processes (C&EN, Oct. 7, page 13). The noted improvements rely in part on standards that ensure the accuracy of climate models, and the International Union of Pure & Applied Chemistry plays a key role in keeping these standards up to date. A critical component of climate prediction is atmospheric chemistry and researchers’ ability to model it. An IUPAC committee has issued six studies on atmospheric chemistry over the past decade, each one focusing on different sets of reactions that occur in the atmosphere. The latest effort looks at heterogeneous processes involving airborne liquid particles (Atmos. Chem. Phys. 2013, DOI: 10.5194/acp-13-8045-2013). Atmospheric modeling is important not just for regional and global climate prediction but also for understanding phenomena such as air ...

MJO prediction in the NCEP Climate Forecast System version 2

The Madden–Julian Oscillation (MJO) is the primary mode of tropical intraseasonal climate variability and has significant modulation of global climate variations and attendant societal impacts. Advancing prediction of the MJO using state of the art observational data and modeling systems is thus a necessary goal for improving global intraseasonal climate prediction. MJO prediction is assessed in the NOAA Climate Forecast System version 2 (CFSv2) based on its hindcasts initialized daily for 1999–2010. The analysis focuses on MJO indices taken as the principal components of the two leading EOFs of combined 15°S–15°N average of 200-hPa zonal wind, 850-hPa zonal wind and outgoing longwave radiation at the top of the atmosphere. The CFSv2 has useful MJO prediction skill out to 20 days at which the bivariate anomaly correlation coefficient (ACC) drops to 0.5 and root-mean-square error (RMSE) increases to the level of the prediction with climatology. The prediction skill also shows a seasonal variation with the lowest ACC during the boreal summer and highest ACC during boreal winter. The prediction skills are evaluated according to the target as well as initial phases. Within the lead time of 10 days the ACC is generally greater than 0.8 and RMSE is less than 1 for all initial and target phases. At longer lead time, the model shows lower skills for predicting enhanced convection over the Maritime Continent and from the eastern Pacific to western Indian Ocean. The prediction skills are relatively higher for target phases when enhanced convection is in the central Indian Ocean and the central Pacific. While the MJO prediction skills are improved in CFSv2 compared to its previous version, systematic errors still exist in the CFSv2 in the maintenance and propagation of the MJO including (1) the MJO amplitude in the CFSv2 drops dramatically at the beginning of the prediction and remains weaker than the observed during the target period and (2) the propagation in the CFSv2 is too slow. Reducing these errors will be necessary for further improvement of the MJO prediction.

Extreme Daily Rainfall Event Distribution Patterns in Kansas

AbstractThe Rainfall Frequency Atlas (TP40) was last updated for Kansas in 1961, using weather data from 1911 to 1958. Rainfall information contained in the atlas is the basis for important engineering and hydrologic design decisions in the state. With growing concern about the effects of global climate change and predictions of more extreme weather events, it is necessary to explore rainfall distribution patterns using the most current and complete data available. In this study, extreme rainfall frequency was analyzed using daily precipitation data (1920–2009) from 24 stations in Kansas and 15 stations from adjacent states. The Weibull distribution was used to calculate the precipitation probability distribution frequency at each station. Weather station point data were spatially interpolated using kriging. The overall analysis showed an increase in extreme precipitation events in Kansas with extreme event values tending to increase in magnitude from the northwest to southeast part of the state. Comparin...

African Rainfall Climatology Version 2 for Famine Early Warning Systems

AbstractThis paper describes a new gridded, daily 29-yr precipitation estimation dataset centered over Africa at 0.1° spatial resolution. Called the African Rainfall Climatology, version 2 (ARC2), it is a revision of the first version of the ARC. Consistent with the operational Rainfall Estimation, version 2, algorithm (RFE2), ARC2 uses inputs from two sources: 1) 3-hourly geostationary infrared (IR) data centered over Africa from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) and 2) quality-controlled Global Telecommunication System (GTS) gauge observations reporting 24-h rainfall accumulations over Africa. The main difference with ARC1 resides in the recalibration of all Meteosat First Generation (MFG) IR data (1983–2005). Results show that ARC2 is a major improvement over ARC1. It is consistent with other long-term datasets, such as the Global Precipitation Climatology Project (GPCP) and Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP), with correlation coefficients of 0.86 over a 27-yr period. However, a marginal summer dry bias that occurs over West and East Africa is examined. Daily validation with independent gauge data shows RMSEs of 11.3, 13.4, and 14, respectively, for ARC2, Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis 3B42, version 6 (3B42v6), and the CPC morphing technique (CMORPH) for the West African summer season. The ARC2 RMSE is slightly higher for Ethiopia than those of CMORPH and 3B42v6. Both daily and monthly validations suggested that ARC2 underestimations may be attributed to the unavailability of daily GTS gauge reports in real time, and deficiencies in the satellite estimate associated with precipitation processes over coastal and orographic areas. However, ARC2 is expected to provide users with real-time monitoring of the daily evolution of precipitation, which is instrumental in improved decision making in famine early warning systems.

Zonal Ecosystems of Volga River Basin under Global Warming: Prognostic and Paleogeographical Scenarios

The concept of landscape-ecological prognoses for temperate forest ecosystems in light of forthcoming climate changes is presented. Predictive analysis is based on empirical-statistical modeling ecosystems and on descriptions of their space organization. The boreal bioclimatic ecotone of the Volga river basin is used as an example for carry out the experience of analytical and cartographic prognostic scenario modeling of the nearest and distant future for given ecoregion and their paleogeographical analogs in upper Pleistocene and Holocene. The mechanisms of local and regional response of forest ecosystems to the anticipated global warming in the 21st centuries have been assessed, according to global climate prediction model GISS-1993 (modernized version 2003–2006). The quantitative methods of landscape-ecological forecasts have been developed by the author are described in detail. A progressively increasing thermo-arid bioclimatic trend is predicted, with a general shift of zonal boundaries to the north and with the corresponding changes in the water regime and plant cover structure of the territory. An analogous picture results from paleo-prognostic reconstructions of the optima of Holocene and Mikulino interglacial period.

Statistical downscaling of climate forecast system seasonal predictions for the Southeastern Mediterranean

Most of the annual rainfall in the Southeastern Mediterranean falls in the wet season from November to March. It is associated with Mediterranean cyclones, and is sensitive to climate variability. Predicting the wet season precipitation with a few months advance is highly valuable for water resource planning and climate-associated risk management in this semi-arid region. The regional water resource managements and climate-sensitive economic activities have relied on seasonal forecasts from global climate prediction centers. However due to their coarse resolutions, global seasonal forecasts lack regional and local scale information required by regional and local water resource managements. In this study, an analog statistical-downscaling algorithm, k-nearest neighbors (KNN), was introduced to bridge the gap between the coarse forecasts from global models and the needed fine-scale information for the Southeastern Mediterranean. The algorithm, driven by the NCEP Climate Forecast System (CFS) operational forecast and the NCEP/DOE reanalysis, provides monthly precipitations at 2–4months of lead-time at 18 stations within the major regional hydrological basins. Large-scale predictors for KNN were objectively determined by the correlations between the station historic daily precipitation and variables in reanalysis and CFS reforecast. Besides a single deterministic forecast, this study constructed sixty ensemble members for probabilistic estimates. The KNN algorithm demonstrated its robustness when validated with NCEP/DOE reanalysis from 1981 to 2009 as hindcasts before applied to downscale CFS forecasts. The downscaled predictions show fine-scale information, such as station-to-station variability. The verification against observations shows improved skills of this downscaling utility relative to the CFS model. The KNN-based downscaling system has been in operation for the Israel Water Authority predicting precipitation and driving hydrologic models estimating river flow and aquifer charge for water supply.

The NASA-Goddard Multi-scale Modeling Framework–Land Information System: Global land/atmosphere interaction with resolved convection

The present generation of general circulation models (GCM) use parameterized cumulus schemes and run at hydrostatic grid resolutions. To improve the representation of cloud-scale moist processes and land–atmosphere interactions, a global, Multi-scale Modeling Framework (MMF) coupled to the Land Information System (LIS) has been developed at NASA-Goddard Space Flight Center. The MMF–LIS has three components, a finite-volume (fv) GCM (Goddard Earth Observing System Ver. 4, GEOS-4), a 2D cloud-resolving model (Goddard Cumulus Ensemble, GCE), and the LIS, representing the large-scale atmospheric circulation, cloud processes, and land surface processes, respectively. The non-hydrostatic GCE model replaces the single-column cumulus parameterization of fvGCM. The model grid is composed of an array of fvGCM gridcells each with a series of embedded GCE models. A horizontal coupling strategy, GCE ↔ fvGCM ↔ Coupler ↔ LIS, offered significant computational efficiency, with the scalability and I/O capabilities of LIS permitting land–atmosphere interactions at cloud-scale. Global simulations of 2007–2008 and comparisons to observations and reanalysis products were conducted. Using two different versions of the same land surface model but the same initial conditions, divergence in regional, synoptic-scale surface pressure patterns emerged within two weeks. The sensitivity of large-scale circulations to land surface model physics revealed significant functional value to using a scalable, multi-model land surface modeling system in global weather and climate prediction.

Horizontal grids for global weather and climate prediction models: a review

AbstractA latitude–longitude grid is used by almost all operational atmospheric forecast models, and many research models. However, it is expected that the advantages of a latitude–longitude grid will become outweighed on massively parallel computers by data‐communication bottlenecks. There is therefore renewed interest in quasi‐uniform alternatives. This review surveys and assesses previously proposed horizontal grids for modelling the atmosphere over the sphere. Aspects of numerical accuracy likely to be affected by grid structure are discussed; particular attention is paid to computational modes and grid imprinting. Computational modes are potentially very serious, since they may be excited in realistic applications by boundary conditions, nonlinearity, physical forcing, and data assimilation. The geometry of polyhedra is reviewed due to its relation to numerical degrees of freedom, and hence to numerical wave dispersion and the possible existence of computational modes.All grids proposed to date have known problems or issues that merit further investigation. Orthogonal logically rectangular grids may be generated using conformal maps, but these suffer from singularities and resolution clustering. Resolution clustering may be avoided by using overset grids, but there are potential issues associated with the overlap regions. Alternatively, resolution clustering may be avoided, whilst retaining a logically rectangular grid, by giving up orthogonality; however, existing numerical schemes exploit orthogonality to obtain various properties thought to be important for accuracy, and it is not yet known whether these can also be obtained on non‐orthogonal grids. Quasi‐uniformity and orthogonality can be obtained without resolution clustering or overlaps by using non‐quadrilateral grid cells, such as triangles, or pentagons and hexagons. However, when a staggered placement of variables is used to minimise dispersion errors for fast waves, non‐quadrilateral grids support computational modes.In view of the lack of a single ideal grid, several topics meriting further investigation are identified. Copyright © 2011 Royal Meteorological Society and British Crown Copyright, the Met Office

Seasonal Variations in Hydrological Influences on Gravity Measurements Using gPhones

Hydrological influences on a local gravity field may reach amplitudes on the order of 10 microgals. Since 2007, fifteen Microg LaCoste gPhones have been successively installed in gravity stations in China. The outputs from gPhones include ten data channels with second sampling such as raw gravity, corrected gravity, long level data and cross level data, ambient and sensor temperature, ambient and sensor pressure, and others. In this study, we select six stations in northwest China (GaoTai, LaSa, LanZhou, ShiQuanHe, WuShi, XiAn) and one station in the northeast (HaiLaEr). We have modeled the major tides (earth solid tide, ocean tide and pole tide), corrected for atmospheric loading effects using local measurements, fitted instrumental drift using segmental fitting based on the distinct characteristics of gravimeter drift, and ultimately obtained the monthly residual gravity with amplitudes of 10 ~ 20 microgals. We find that the results obtained by the gravimeter for those stations with stable conditions and no large disturbances are obviously correlated with hydrologic loading as modeled by the Global Land Data Assimilation System and Climate Prediction Center. We also notice that at some stations there are obvious phase lags with a period of three months or more between the residual gravity and the influence of hydrological loading. These large discrepancies may be associated with local hydrologic effects, local topography or some other complex tectonic movement and geodynamical mechanism, which were not considered in this paper.

GRACE and AMSR‐E‐based estimates of winter season solid precipitation accumulation in the Arctic drainage region

Solid precipitation plays a major role in controlling the winter hydrological cycle and spring discharge in the Arctic region. However, it has not been well documented due to sharply decreasing numbers of precipitation gauges, gauge measurement biases, as well as limitations of conventional satellite methods in high latitudes. In this study, we document the winter season solid precipitation accumulation in the Arctic region using the latest new satellite measurements from the Gravity Recovery and Climate Experiment (GRACE) and the Advanced Microwave Scanning Radiometer‐Earth Observing System (AMSR‐E). GRACE measures the winter total water (mainly from snow water equivalent (SWE)) storage change through gravity changes while AMSR‐E measures the winter SWE through passive microwave measurements. The GRACE and AMSR‐E measurements are combined with in situ and numerical model estimates of discharge and evapotranspiration to estimate the winter season solid precipitation accumulation in the Arctic region using the water budget equation. These two satellite‐based estimates are then compared to the conventional estimates from two global precipitation products, such as the Global Precipitation Climatology Project (GPCP) and Climate Prediction Center's Merged Analysis of Precipitation (CMAP), and three reanalyses, the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis, the European Centre for Medium‐Range Weather Forecasts' ERA‐Interim, and the Japan Meteorological Agency's Climate Data Assimilation System (JCDAS) reanalysis. The GRACE‐based estimate is very close to the GPCP and ERA‐Interim estimates. The AMSR‐E‐based estimate is the most different from the other estimates. This GRACE‐based measurement of winter season solid precipitation accumulation can provide a new valuable benchmark to understand the hydrological cycle, to validate and evaluate the model simulation, and to improve data assimilation in the Arctic region.